Draft Risk Assessment for the Petroleum Refining Source Sector – FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 1



## Draft Residual Risk Assessment for th e Petroleum Refining Source Sector

by EPA’s Office of Air Quality Planning and Standards Office of Air and Radiation May 2014 Deleted: FebruaryDraft Risk Assessment for the Petroleum Refining Source Sector – FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 2



### Table of Contents

1 Introduction .................................................................................................................. ...... 4
2 Methods ....................................................................................................................... ....... 4

2.1 Emissions and source data ........................................................................................... 4
2.2 Dispersion modeling for inhalation exposure assessment ........................................... 5
2.3 Estimating chronic human inhalation exposure .......................................................... 8
2.4 Acute Risk Screening and Refined Assessments ........................................................ 9
2.5 Multipathway human health and enviro nmental risk scr eening .............. .................. 10
2.6 Dose-response assessment ......................................................................................... 14

2.6.1 Sources of chronic dose-response information .................................................. 14
2.6.2 Sources of acute dose-response information ...................................................... 25
2.7 Risk characterization ................................................................................................. 29

2.7.1 General ............................................................................................................... 29
2.7.2 Mixtures ............................................................................................................. 31
2.7.3 Facility-wide Risks ..........

................................ ................................ .................. 31
2.7.4 MACT-Allowable Risk ...................................................................................... 32 3 Risk results for the petroleum refining source sector ......................................................... 32

3.1 Source category emissions and results ...................................................................... 32
3.2 Baseline risk characterization .................................................................................... 37
3.3 Post-control risk characterization .............................................................................. 48
4 General discussion of uncertainties and how they have been addressed ......................... 49

4.1 Exposure modeling uncertainties .............................................................................. 49
4.2 Uncertainties in the dose-response relationships ....................................................... 50
5 References .................................................................................................................... .... 59

Index of Tables

Table 2.2-1 AERMOD version 12345 model options for RTR modeling ................................ 6 Table 2.6-1 (A) Dose-Response Values for ............................................................................ 18 Chronic Inhalation Exposure to Carcinogens .......................................................................... 18 Table 2.6-1 (B) Dose-Response Values for Chronic Oral Exposure to Carcinogens ............. 21 Table 2.6-2 (A) Dose-Response Values for Ch ronic Inhalation Exposure to Noncarcinogens

.............................................................................................................................. .................... 22 Table 2.6-2 (B) Dose-Response Values for Chronic Oral Exposure to Noncarcinogens ....... 25 Table 2.6-3 Dose-Response Values for Acute Exposure ........................................................ 27 Table 3.2-5 Source Category Contribu tion to Facility-Wide

................................ .................. 48 Chronic Cancer Risks (Actual Emissions) ............................................................................... 48

Appendices

Appendix 1 Technical Support Document for HEM3 Modeling Appendix 2 Meteorological Data for HEM3 Modeling Appendix 3 Analysis of data on short-term emission rates relative to long-term emission

ratesDraft Risk Assessment for the Petroleum Refining Source Sector – FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 3

Appendix 4 Technical Support Document for TRIM-Based Multipathway Tiered Screening Methodology for RTR Appendix 5 Detailed Ri sk Modeling Results Appendix 6 Acute Impacts Refined Analysis Appendix 7 Dispersion Model Receptor Re visions and Additions for the Petroleum Refinery Source Sector Appendix 8 Development of the Chroni c Screening Level for Carbonyl Sulfide Appendix 9 Refinery Emissions and Risk Es timates for Modeled “Allowable” Emissions Appendix 10 Technical Support Document: Human Health Multipathway Residual Risk Assessment for the Petroleum Refineries Sector - A Site Specific Analysis Appendix 11 Environmental Risk Screen

Index of Acronyms

AERMOD American Meteorological Society/EPA Regulatory Model AEGL Acute exposure guideline level ASTDR US Agency for Toxic Substances and Disease Registry CalEPA California Environmental Agency CTE Central Tendency Estimate ERPG Emergency Response Planning Guideline HAP Hazardous Air Pollutant HEM Human Exposure Model HI Hazard index HQ Hazard quotient IRIS Integrated Risk Information System MACT Maximum Achievable Control Technology MIR Maximum Individual Risk MOA Mode of action NAC National Advisory Committee NAAQS National Ambient Air Quality Standards NATA National Air Toxics Assessment NEI National Emissions Inventory NPRM Notice of Proposed Rulemaking PB-HAP Persistent and Bioaccumulative - HAP POM Polycyclic organic matter REL Reference exposure level RfC Reference concentration RfD Reference dose RME Reasonable Maximum Exposure RTR Risk and Technology TOSHI Target-organ-specific hazard index URE Unit risk estimateDraft Risk Assessment for the Petroleum Refining Source Sector – FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 4



### 1 Introduction

Section 112 of the Clean Air Act (CAA) establishes a two-stage regulatory process for addressing emissions of hazardous air pollutants (HAP) from stationary sources. In the first stage, section 112(d) requires the Environmental Protection Agency (EPA, or the Agency) to develop technology-based standards for categories of sources (e.g., petroleum refineries, pulp and paper mills, etc.) [ 1 ]. EPA has largely completed the initial Maximum Achievable Control Technology (MACT) standards as required under this provision. Under section 112(d)(6), EPA must review each of these technology-based standards at least every eight years and revise a standard, as necessary, “taking into account developments in practices, processes and control technologies.” In the second stage, EPA is required under section 112(f)(2) to assess the health and environmental risks that remain after implementation of the MACT standards. If additional risk reductions are necessary to protect public health with an ample margin of safety or to prevent an adverse environmental effect, EPA must develop standards to address these remaining risks. Th is second stage of the regulatory process is known as the residual risk stage. For each source category for which EPA issued MACT standards, the residual risk stage must be comp leted within eight years of promulgation of the initial technology-based standard.

In December of 2006 we consulted with a panel from the EPA's Science Advisory Board (SAB) on the “Risk and Technology Review (RTR ) Assessment Plan,” and in June of 2007, we received a letter with the results of that co nsultation. Subsequent to the consultation, in June of 2009, a meeting was held with an SAB panel for a formal peer review of the “Risk and Technology Review (RTR) Assessment Methodologies” [ 2 ]. We received the final SAB report on this review in May of 2010 [ 3 ]. Where appropriate, we have responded to the key messages from this review in developing our current risk assessments and we will be continuing our efforts to improve our assessmen ts by incorporating updates based on the SAB recommendations as they are developed and become available. Our responses to the key recommendations of the SAB are outlined in a me mo entitled, “EPA’s Actions in Response to Key Recommendations from the SAB Review of RTR risk Assessment Methodologies” [ 4 ].

This document contains the methods and the results of baseline risk assessments (i.e., after the implementation of the MACT standard) and the results of the post-control scenario risk assessment performed for the Petroleum Refining Source Sector. The methods discussion includes descriptions of the methods used to develop refined estimates of chronic inhalation exposures and human health risks for cancer and noncancer endpoints, as well as descriptions of the methods used to screen for acute health risks, chronic non-inhalation health risks, and adverse environmental effects.



### 2 Methods

2.1 Emissions and source data The first and primary source category for which regulations were developed, Petroleum Refineries - Other Sources Not Distinctly Li sted (Refinery MACT 1), includes all emission sources from petroleum refinery process units except those that were expected to be regulated elsewhere, such as the NESHAP for Boilers and Process Heaters (40 CFR part 63 subpartDraft Risk Assessment for the Petroleum Refining Source Sector – FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 5

DDDDD). The Refinery MACT 1 rule, which focuses on evaporative emissions, was published in 40 CFR part 63, subpart CC on August 18, 1995 (60 FR 43620) and specifically excludes three types of process vents: catal ytic cracking unit (CCU) catalyst regeneration vents, catalytic reforming unit (CRU) catalyst regeneration vents, and sulfur plant vents. These specific vents are regulated by the NESHAP for Petroleum Refineries: Catalytic Cracking Units, Catalytic Reforming Units, and Su lfur Recovery Units (Refinery MACT 2) in 40 CFR part 63, subpart UUU. The Refinery MACT 2 standards, which focus on combustion sources, were promulgated on April 11, 2002 (67 FR 17762. This analysis will evaluate the risks associated with both the MACT 1 and MACT 2 emissions.

In support of the residual risk and technology review required by CAA sections 112(f) and 112(d)(6) for this source sector, on April 1, 2011 EPA sent out an information collection request (ICR) under the authority of Section 114 of the CAA to the petroleum refining industry. The ICR requested available inform ation regarding process equipment, control devices, point and fugitive emissions, practices used to control fugitive emissions, and other aspects of facility operations, including stack parameters and locations. Details on the development of the emissions and source data for this source category can be found in the docket.[ 5 ] Section 3 below provides a summary of the emissions.



### 2.2 Dispersion modeling for inhalation exposure assessment
Both long- and short-term inhalation exposure concentrations and associated health risks from each facility in the source category were estimated using the Human Exposure Model in combination with the American Meteorologica l Society/EPA Regulatory Model dispersion modeling system (HEM-AERMOD, or HEM3). The approach used in applying this modeling system is outlined below, and further details are provided in Appendix 1 to this document ( Technical Support Document for HEM3 Modeling ). The HEM3 performs three main operations: atmospheric dispersion modeling, estimation of individual human exposures and health risks, and estimation of population risks. This section focuses on the dispersion modeling component. The exposure and risk characterization components are discussed in other subsections of Sections 2 and 3.

The dispersion model in the HEM3 system, AERMOD version 12345 (the version of AERMOD that was available at the time of th e risk analysis), is a state-of-the-science Gaussian plume dispersion model that is preferred by EPA for modeling point, area, and volume sources of continuous air emissi ons from facility applications [ 6 ]. Further details on AERMOD can be found in the AERMOD Users Guide [ 7 ]. The model is used to develop annual average ambient concentrations through the simulation of hour-by-hour dispersion from the emission sources into the surrounding atmosphere. Hourly emission rates used for this simulation are generated by evenly dividing the total annual emission rate from the inventory into the 8,760 hours of the year.

The first step in the application of the HEM3 modeling system is to predict ambient concentrations at locations of interest. The AERMOD model options employed are summarized in Table 2.2-1 and are discussed further below.Draft Risk Assessment for the Petroleum Refining Source Sector – FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 6

Table 2.2-1 AERMOD version 12345 model options for RTR modeling

Modeling Option Selected Parameter for chronic exposure

Type of calculations Hourly Ambient Concentration

Source type Point, area represented as pseudo point source

Receptor orientation Polar (13 rings and 16 radials) Discrete (census block centroids) and user-supplied receptors

Terrain characterization Actual from USGS 1-degree DEM data

Building downwash Not Included

Plume deposition/depletion Not Included

Urban source option No

Meteorology 1 year representative NWS from nearest site ( 824 stations)

In HEM3, meteorological data are ordinarily selected from a list of over 800 National Weather Service (NWS) surface observation sta tions across the continental United States, Alaska, Hawaii, and Puerto Rico. In most cases the nearest station is selected as representative of the conditions at the subject facility. When considering off-site meteorological data most site specific dispersion , EPA modeling guidelines suggest that modeling efforts employ up to five years of da ta to capture variabili ty in weather patterns from year to year. However, because dispersion model runtimes using five years of meteorological data would be too long for RTR source categories with many sources, wemodeled only a single year, 2011. While the selection of a single year may result in underprediction of long-term ambient levels at some locations, likewise it may result in overprediction at others. For each facility identifi ed by its characteristic latitude and longitude coordinates, the closest meteorological station was used in the dispersion modeling. The average distance between a modeled facility an d the applicable meteorological station was 22 miles. Appendix 2 to this document ( Meteorological Data Processing Using AERMET for
HEM3 ) provides a complete listing of stations an d assumptions along with further details used in processing the data through AERMET, the AERMOD meteorological data processing

program. The document “Risk and Technology Review (RTR) Risk Assessment Methodologies: For Review by the EPA’s Scie nce Advisory Board with Case Studies – MACT I Petroleum Refining Sources and Portland Cement Manufacturing” (US EPA, 2009) provides more details on these modeling approaches.

Beginning with version 12345, AERMET allows for a minimum wind speed of 0.5 m/s to be utilized when processing monitoring stations th at are equipped with sonic anemometers and for an adjustment to the surface friction velo city (u*). The public version of AERMET available at the time we conducted the modeling did not include the surface friction velocity adjustment. Also at the time, an updated version of AERMET (and AERMOD) was under development that incorporated further surface friction velocity adjustments to AERMETversion 12345. In anticipation of the update, EPA processed meteorological data using a prepublic release version of AERMET and incorporated the updated surface friction velocity Deleted: Ideally, when

Deleted: willDraft Risk Assessment for the Petroleum Refining Source Sector – FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 7

adjustment in the output. It was EPA’s judgement that the pre-public release version of AERMET would generate AERMOD ready meteorological data very close to that generated with the final public release version.

EPA has posted the AERMET meteorological data used in the refinery analysis on the EPA’s FERA (Fate, Exposure, and Risk Analysis) website under the HEM model page. AERMET and AERMOD (version 13350) were released to the public in late December 2013 on the EPA’s Support Center for Regulatory Atmospheric Modeling (SCRAM) website.

While it was not feasible to remodel the entire source category using the public released version of AERMOD and AERMET, we were able to compare modeling results for 14 refineries using the using meteorological data processed with the pre-public release version of AERMET and data processed with the current 13350 version. In addition, the 13350 version of AERMOD was run with the meteorological data processed with the 13350 version of AERMET. The comparison was conducted by examining the maximum individual risk (MIR) at 14 refinery facilities, including the 4 facilities with the highest risk, the 5 with the lowest risks, and 5 randomly selected facilities. The largest difference in MIR values between the runs was 3 percent for one facility. The other 13 facilities had differences in their MIR values between runs of less than 1 percent. These results of this comparison are presented in Appendix 5.

The HEM3 system estimates ambient concentrat ions at the geographic centroids of census blocks (using the 2010 Census) and at other receptor locations that can be specified by the user. See Appendix 7 of this document ( Dispersion Model Receptor Revisions and Additions for the Petroleum Refining Sector) for a discussion of user receptors and centroid location changes specific to this source category. The model accounts for the effects of multiple facilities when estimating concentra tion impacts at each block centroid. When evaluating the risks associated with a particular source category we combined the impacts of all facilities within the same source category, and assessed chronic exposure and risk for all census blocks with at least one resident (i.e., locations where people may reasonably be assumed to reside rather than receptor points at the fenceline of a facility). Chronic ambient concentrations were calculated as the annual average of all estimate d short-term (one-hour) concentrations at each block centroid. Possible future residential use of currently uninhabited areas was not considered. Census blocks, the finest resolution available in the census data, are typically comprised of approximately 40 people or about ten households.

In contrast to the development of ambient concentrations for evaluating long-term exposures, which was performed only for occupied census blocks, worst-case short-term (one-hour) concentrations were estimated both at the cen sus block centroids and at points nearer the facility that represent locations where people may be present for short periods, but generally no nearer than 100 meters from the center of the facility (note that for large facilities, this 100-meter ring could still contain locations inside the facility property). Since short-term emission rates were needed to screen for the potential for hazard via acute exposures, and where ICR data only contains annual emission totals. Deleted: Typically

Deleted: only

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The average hourly emissions rate used for chronic risk modeling is defined as the total emissions for a year divided by the total number of operating hours in the year. If a source operated 8,760 hours per year this emissions rate would be appropriate for predicting potential acute impacts. However, many sources in this source sector emit on a more intermittent basis and thus specific hourly emissions may be higher than those utilized for annual or chronic risks modeling. While hourly emissions data for each source are not available, in this assessment, we utilized source category specific engineering judgment to determine what the peak hourly emissions for any given process could be. For processes in which no

“ engineering judgment ” information was available we utili zed a generic factor of ten times actual annual emissions to estimate peak hourly emissions the petroleum refinery specific factors along with the generic factor of ten are discussed in Appendix 3, ( Analysis of data on
short-term emission rates relati ve to long-term emission rates ) of this document.

Census block elevations for HEM3 modeling were determined nationally from the US Geological Service 1-degree dig ital elevation model (DEM) data files, which have a spatial resolution of about 90 meters. Elevations of po lar grid points used in estimating short- and long-term ambient concentrations were assumed to be equal to the highest elevation of any census block falling within the polar grid sector corresponding to the grid point. If a sector does not contain any blocks, the model defaults the elevation to that of the nearest block. If an elevation is not provided for the emission s ource, the model uses the average elevation of all sectors within the i nnermost model ring.

In addition to using receptor elevation to determine plume height, AERMOD adjusts the plume’s flow if nearby elevated hills are expect ed to influence the wind patterns. For details on how hill heights were estimated and used in the AERMOD modeling see Appendix 1 of this document.



### 2.3 Estimating chronic human inhalation exposure We used the estimated annual average ambient air concentration of each HAP, at each census block centroid, as a surrogate for the lifetime inhalation exposure concentration of all the people who reside in the census block. That is, the risk analysis did not consider either the short-term or long-term behavior (mobility) of the exposed populations and its potential influence on their exposure.

We did not address short-term human activity, including indoor air concentrations, for two reasons. First, our experience with the NATA assessments (which modeled daily activity using EPA’s HAPEM model) suggests that, given our current understanding of microenvironment concentrations and daily activities, modeling short-term activity would, on average, reduce risk estimates about 25 percent for particulate HAP; it will also reduce risk estimates for gaseous HAP, but typically by much less. Second, basing exposure estimates on average ambient concentrations at census block centroids may underestimate or overestimate actual exposure concentrations at some residences.

We did not address long-term migration nor population growth or decrease over 70 years, instead basing the assessment on the assumption that each person’s predicted exposure is Deleted: In addition, for past RTR analyses, EPA developed a generic emissions adjustment factor of ten for acute screening

Deleted: was based on

Deleted: . This factor of ten and

Deleted: Further reducing exposure estimates for the most highly exposed residents by modeling their short-term behavior could add a systematic low bias to these results.Draft Risk Assessment for the Petroleum Refining Source Sector – FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 9

constant over the course of their lifetime which is assumed to be 70 years. In assessing cancer risk, we generally estimated three metrics; th e maximum individual risk (MIR), which is defined as the risk associated with a lifetime of exposure at the highest concentration where people are likely to live; the population risk distribution; and the cancer incidence. The assumption of not considering short or long-term population mobility does not bias the estimate of the theoretical MIR (assumes a person stays in one location for 70 years) nor does it affect the estimate of cancer incidence sin ce the total population number remains the same. It does, however, affect the shape of the distri bution of individual risks across the affected population, shifting it toward higher estimated individual risks at the upper end and reducing the number of people estimated to be at lower risks, thereby increasing the estimated number of people at specific risk levels.

When screening for potentially significant acute exposures, we used an estimate of the highest hourly ambient concentration at any off-site location as the surrogate for the maximum potential acute exposure concentration for any individual. This was generally the Fenceline.



### 2.4 Acute Risk Screening and Refined Assessments
In establishing a scientifically defensible approa ch for the assessment of potential health risks due to acute exposures to HAP, we followed the same general approach that has been used for developing chronic health risk assessments under the residual risk program. That is, we developed a tiered, iterative approach. This approach to risk assessment was endorsed by the National Academy of Sciences in its 1993 publication “Science and Judgment in Risk Assessment” and subsequently was adopted in the EPA’s “Residual Risk Report to Congress” in 1999.

The assessment methodology is designed to eliminate from further consideration those facilities for which we have confidence that no acute adverse health effects of concern will occur. To do so, we use what is called a tiered, iterative approach to the assessment. This means that we began with a screening assessment, which relies on readily available data and uses conservative assumptions that in combination approximate a worst-case exposure. The result of this screening process is that either the facility being assessed poses no potential acute health risks (i.e., it “screens out”), or that it requires further, more refined assessment. A more refined acute assessment could use industry- or site-specific data on the temporal pattern of emissions, the layout of emission points at the facility, the boundaries of the facility, and/or the local meteorology. In some cases, all of these site-specific data are used to refine the assessment; in others, lesser amounts of site-specific data can be used to determine that acute exposures are not a concern, and significant additional data collection is not

necessary.

Acute health risk screening was performed for each facility as the first step. We used conservative assumptions for emission rates, meteorology, and exposure location. We used the following worst-case assumptions in our screening approach:

 Peak 1-hour emissions were obtained from the ICR and based on the operating characteristics of refinery emission s ources as described in Appendix 3.Draft Risk Assessment for the Petroleum Refining Source Sector – FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 10

 We assumed that the peak emissions occur at all emission points at the same time.

 For facilities with multiple emission po ints, 1-hour concentrations at each receptor were assumed to be the sum of the maximum concentrations due to each emission point, regardless of whether those maximum concentrations occurred during the same hour.

 Worst-case meteorology (from one year of local meteorology) was assumed to occur at the same time the peak emission rates occur. The recommended EPA local-scale dispersion model, AERMOD, is used for simulating atmospheric dispersion.

 A person was assumed to be located downwind at the point of maximum modeled impact during this same worst-case 1-hour period, but no nearer to the source than 100

meters.

 The maximum impact was compared to multiple short-term health benchmarks for the HAP being assessed to determine if a possible acute health risk might exist. These benchmarks are described in section 2.6 of this report.

As mentioned above, when we identify acute impacts which exceed their relevant benchmarks, we pursue refining our acute screening estimates to the extent possible. In some cases, this may include the use of a f acility-specific emissions multiplier to estimate the peak hourly emission rates from the average rates (rather than the default factor of 10). In other cases, this may entail determining the actual physical layout and boundaries of a facility to more accurately gauge where people might reasonably be exposed for an hour.

For the petroleum refinery source category, the refinement included a review of the layout of the emission points at the facilities with th e facility boundaries to determine the maximum off-site acute impact for the facilities that did not screen out during the initial run. We also used petroleum refinery specific factors, as described above. Refer to Appendices 5 and 6 for the detailed results for these sites.



### 2.5 Multipathway human health and environmental risk analysis

The EPA conducted a screening analysis examining the potential for significant human health risks due to exposures via routes other than inhalation (i.e., ingestion). We first determined whether any sources in the source categories emitted any hazardous air pollutants known to be persistent and bio-accumulative in environment (PB-HAP) 1 . The PB-HAP compounds or compound classes are identified for the scr eening from the EPA’s Air Toxics Risk Assessment Library [ 8 ]. With respect to PB-HAP emissi ons other than lead, emissions were evaluated for potential non-inhalation risks impacts using a Tiered screening approach. We first determined whether the facility-specific em ission rates of each of the emitted PB–HAP were large enough to create the potential for significant non-inhalation human health risks under reasonable worst-case conditions. To facilitate this step, we developed emissions rate

1 Although the two-letter chemical sym bol for lead is Pb, in this assessment PB-HAP refers to the many air pollutants known to be persistent and bioaccumulative in the environment. In instances where the report is specifically referring to lead, it is spelled out (i.e., the tw o-letter chemical symbol for lead is not used in this document).Draft Risk Assessment for the Petroleum Refining Source Sector – FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 11

thresholds for each PB–HAP using a hypothetical upper-end screening exposure scenario developed for use in conjunction with the EPA’s Total Risk Integrated Methodology. Fate, Transport, and Ecological Exposure (TRIM.FaTE[ 9 ]) model. We conducted a sensitivity analysis on the screening scenario to ensure th at its key design parameters would represent the upper end of the range of possible values, such that it would represent a conservative but not impossible scenario. The facility-specific em issions rates of each of the PB–HAP were compared to the emission rate threshold valu es for each of the PB–HAP identified to assess the potential for significant human health ri sks via non-inhalation pathways. We call this application of the TRIM.FaTE model the Tier I TRIM- Screen. For the purpose of developing emissions rates for our Tier I TRIM-Screen, we derived emission levels for each PB-HAP (other than lead) at which the maximum excess lifetime cancer risk would be 1-in-1 million or, for HAP that cause non-cancer health effects, the maximum HQ would be 1. If the emissions rate of any PB-HAP exceeds the Tier I screening emissions rate for any facility, we conduct a Tier II multi-pathway screen. In the Tier II screen, the location of each facilit y that exceeded the Tier I emissi on rate is used to refine the assumptions associated with the environmental scenario while maintaining the exposure scenario assumptions. We then adjust the risk -based Tier I screening level for each PB-HAP for each facility based on an understanding of how exposure concentrations estimated for the screening scenario change with meteorology and environmental assumptions. PB-HAP emissions that do not exceed these new Tier II screening levels are considered to pose no unacceptable risks. When facilities exceed the Ti er II screening levels, it does not mean that multi-pathway impacts are significant, only that we cannot rule out that possibility based on the results of the screen. Thes e facilities may be further eval uated for multi-pathway risks using the TRIM.FaTE model and more site specific information. Appendix 4 of this document (Technical Support Document for TRIM-Based Multipathway Tiered Screening Methodology for RTR) contains a complete discussion of the multipathway Tier I and II screening approach. A detailed discussion of the site specific multipathway approach is discussed in Appendix 10, “Technical S upport Document: Human Health Multipathway Residual Risk Assessment for the Petroleum Refi neries Sector –A Site Specific Analysis.”

In evaluating the potential multipathway risks from emissions of lead compounds, rather than developing a screening emission rate for them, we compared maximum estimated chronic atmospheric concentrations with the curren t National Ambient Air Quality Standard (NAAQS) for lead. Values below the NAAQS we re considered to have a low potential for multipathway risks of any significance.

The NAAQS value, a public health policy judg ment, incorporated the Agency’s most recent health evaluation of air effects of lead expos ure for the purposes of setting a national ambient air quality standard. In setting this value, th e Administrator promulgated a standard that was requisite to protect public health with an adequate margin of safety. We consider values below the level of the primary NAAQS to prot ect against multipathway risks because as mentioned above, the primary NAAQS is set as to protect public health with an adequate margin of safety. However, ambient air lead concentrations above the NAAQS are considered to pose the potential for increased risk to public health. We consider this NAAQS assessment to be a refined analysis given: 1) the numerous health studies, detailed risk and exposure analyses, and level of external peer and public review that went into theDraft Risk Assessment for the Petroleum Refining Source Sector – FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 12

development of the primary NAAQS for lead, combined with: 2) the site-specific dispersion modeling used in this assessment to estimated ambient lead concentrations due to the source category emissions. It should be noted, however, that this comparison does not account for possible population exposures to lead from sources other than the one being modeled; for example, via consumption of water from untreated local sources or ingestion of locally grown food. Nevertheless, the Administrator judged th at such a standard would protect, with an adequate margin of safety, the health of children and other at-risk populations against an array of adverse health effects, most notably in cluding neurological effects, particularly neurobehavioral and neurocognitive effects, in children (73 FR 67007). The Administrator, in setting the standard, also recognized that no ev idence-or risk based bright line indicated a single appropriate level. Instead a collection of scientific evidence and other information was used to select the standard from a range of reasonable values (73 FR 67006).

We further note that comparing ambient lead concentrations to the NAAQS for lead, considering the level, averaging time, form a nd indicator, also informs whether there is the potential for adverse environmental effects. Th is is because the secondary lead NAAQS, set to protect against adverse welfare effects (including adverse environmental effects), has the same averaging time, form, and level as the primary standard. Thus, ambient lead concentrations above the NAAQS for lead also indicate the potential for adverse environmental effects.

Adverse environmental effects The EPA has developed a screening approach to examine the potential for adverse environmental effects as required under section 112(f)(2)(A) of the CAA. The environmental screen focuses on the following seven environmental HAP:

 Five persistent bioaccumulative HAP (PB-HAP) – cadmium, dioxins/furans, polycyclic organic matter (POM), mercury (both inorganic mercury and methyl mercury) and lead.

 Two acid gases - hydrogen chloride (H Cl), and hydrogen fluoride (HF).

HAP that persist and bioaccumulate are of particular environmental concern because they accumulate in the soil, sediment and water. The acid gases, HCl and HF, were included due to their well-documented potential to cause direct damage to terrestrial plants.

For the environmental risk screen ing analysis, EPA first determined whether any facilities in the source category emitted any of the seven environmental HAP. If one or more of the seven environmental HAP evaluated are emitted by at least one facility in the source category we proceed to the second step of the evaluation.

For cadmium, mercury, POM and dioxins/furans, the environmental screening analysis consists of two tiers. In the first tier, TRIM.FaTE modeling was conducted under worst-cast environmental conditionals to determine whethe r the facility-specific off-site emission rates of each of the emitted environmental HAP exceeded ecological benchmarks represented as emissions threshold values. If off-site emissi ons from a facility do not exceed the Tier I thresholds the facility “passes” the screen, and therefore, is not evaluated further under theDraft Risk Assessment for the Petroleum Refining Source Sector – FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 13

screening approach. If off-site emissions from a facility exceed the Tier I thresholds, we evaluate the facility further in Tier II.

In Tier II of the environmental screening analysis, the screening emission thresholds are adjusted to account for local meteorology and the actual location of lakes in the vicinity of facilities that did not pass the Tier I screen. If off-site emissions from a facility do not exceed the Tier II thresholds, the facility passes the screen, and is typically not evaluated further. If off-site emissions from a facility exceed the Tier II thresholds, the facility does not pass the screen and, therefore, may have the potential to cause adverse environmental effects. Such facilities are evaluated further to investigate fa ctors such as the magnitude and characteristics of the area of exceedance.

For acid gases, the environmental screening anal ysis evaluates the potential phytotoxicity and reduced productivity of plants due to chronic exposure to acid gases. The environmental risk screening methodology for acid gases is a single-tier screen that compares the average off-site ambient air concentration over the modeling domain to ecological benchmarks for each of the acid gases. For purposes of ecological risk screening, EPA identifies a potential for adverse environmental effects to plant communities fr om exposure to acid gases when the average concentration of the HAP around a facility exceeds the LOAEL ecological benchmark. In such cases, we further investigate factors such as the magnitude and characteristics of the area of exceedance (e.g., land use of exceedance area, size of exceedance area) to determine if there is an adverse environmental effect.

For lead compounds, we currently do not have the ability to calculate media concentrations using the TRIM.FaTE model. However, air concentrations of lead are already calculated as part of the human health exposure and risk analysis using the HEM-3 AERMOD air dispersion model. To evaluate the potential for adverse environmental effects from lead, we compare the average modeled air emission concentrations of lead from each facility in the source category emissions to the level of the secondary National Ambient Air Quality Standard (NAAQS) for lead. The secondary lead NAAQS is a reasonable means of evaluating environmental risk because it is set to provid e substantial protection against adverse welfare effects which can include “effects on soils, water, crops, vegetation, man-made materials, animals, wildlife, weather, visibility and climate, damage to and deterioration of property, and hazards to transportation, as well as effects on economic values and on personal comfort and well-being.” 2

Ecological benchmarks included in the environm ental risk screen are presented in Appendix
11.

2 A secondary standard, as defined in Section 109(b)(2), must “specify a level of air quality the attainment and maintenance of which, in the judgment of the Administrato r, based on criteria, is requisite to protect the public welfare from any known or anticipated adverse effects as sociated with the presence of [the] pollutant in the ambient air.” Welfare effects as defined in section 30 2(h) (42 U.S.C. 7602(h)) include, but are not limited to, “effects on soils, water, crops, vegetation, man-made materi als, animals, wildlife, weather, visibility and climate, damage to and deterioration of propert y, and hazards to transportation, as well as effects on economic values and on personal comfort and well-being.”Draft Risk Assessment for the Petroleum Refining Source Sector – FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 14



### 2.6 Dose-response assessment

2.6.1 Sources of chronic dose-response information Dose-response assessment (carcinogenic and non-carcinogenic) for chronic exposure (either by inhalation or ingestion) for the HAP report ed in the emissions inventory for the petroleum refinery source sector were based on the EPA Office of Air Quality Planning and Standards’ (OAQPS) existing recommendations for HAP [ 10 ], also used for NATA [ 11 ]. This information has been obtained from various sources and prioritized according to (1) conceptual consistency with EPA risk assessment guidelines and (2) level of peer review received. The prioritization process was aimed at incorporating into our assessments the best available science with respect to dose-response information. The recommendations are based on the following sources, in order of priority:

1) US Environmental Protection Agency (EPA). EPA has developed dose-response assessments for chronic exposure for many of the pollutants in this study. These assessments typically provide a qualitative stat ement regarding the strength of scientific data and specify a reference concentration (RfC, for inhalation) or reference dose (RfD, for ingestion) to protect against effects othe r than cancer and/or a unit risk estimate (URE, for inhalation) or slope factor (SF, for ingest ion) to estimate the probability of developing cancer. The RfC is defined as an “estimate (with uncertainty spanning perhaps an order of magnitude) of a continuous inhalation exposure to the human population (including sensitive subgroups) that is likely to be with out an appreciable risk of deleterious effects during a lifetime.” The RfD is “an estimate (with uncertainty spanning perhaps an order of magnitude) of a daily oral exposure to the human population (including sensitive subgroups) that is likely to be without an appr eciable risk of deleterious effects during a lifetime.” The URE is defined as “the upper-bound excess cancer risk estimated to result from continuous lifetime exposure to an agent at a concentration of 1 μg/m 3 in air.” The SF is “an upper bound, approximating a 95 percent confidence limit, on the increased cancer risk from a lifetime exposure to an agent. This estimate, [is] usually expressed inunits of proportion (of a population) affected per mg/kg-day...” EPA disseminates doseresponse assessment information in several forms, based on the level of review. The Integrated Risk Information System (IRIS) [ 12 ] is an EPA database that contains scientific health assessment information, including dose-response information. All IRIS assessments since 1996 have also undergone independent external peer review. The current IRIS process includes review by EPA scientists, interagency reviewers from other federal agencies, and the public, and peer review by independent scientists external to EPA. EPA’s science policy approach, under the current carcinogen guidelines, is to use linear low-dose extrapolation as a default option for carcinogens for which the mode of action (MOA) has not been identified. We expect future EPA dose-response assessments to identify nonlinear MOAs where appropriate, and we will use those analyses (once they are peer reviewed) in our risk assessments. At this time, however, there are no available carcinogen dose-response assessments for inhalation exposure that are based on a nonlinear MOA

.

2) US Agency for Toxic Substances and Disease Registry (ATSDR). ATSDR, which is part of the US Department of Health and Human Services, develops and publishes MinimumDraft Risk Assessment for the Petroleum Refining Source Sector – FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 15

Risk Levels (MRLs) [ 13 ] for inhalation and oral exposure to many toxic substances. As stated on the ATSDR web site: “Following discussions with scientists within the Department of Health and Human Services (HHS) and the EPA, ATSDR chose to adopt a practice similar to that of the EPA's Refere nce Dose (RfD) and Reference Concentration (RfC) for deriving substance specific health guidance levels for non neoplastic endpoints.” The MRL is defined as “an estimate of daily human exposure to a substance that is likely to be without an appreciable risk of adverse effects (other than cancer) over a specified duration of exposure.” ATSDR describes MRLs as substance-specific estimates to be used by health assessors to select environmental contaminants for further evaluation. Exposures above an MRL do not necessarily represent a threat, and MRLs are therefore not intended for use as predictors of adverse health effects or for setting cleanup levels.

3) California Environmental Protection Agency (CalEPA). The CalEPA Office of Environmental Health Hazard Assessment ha s developed dose-response assessments for many substances, based both on carcinogenicity and health effects other than cancer. The process for developing these assessments is similar to that used by EPA to develop IRIS values and incorporates significant external scientific peer review. As cited in the CalEPA Technical Support Document for developing their chronic assessments [ 14 ]: “The guidelines for developing chronic inhala tion exposure levels incorporate many recommendations of the U.S. EPA [ 15 ] and NAS [ 16 ].” The non-cancer information includes available inhalation health risk guida nce values expressed as chronic inhalation reference exposure levels (RELs) [ 17 ]. CalEPA defines the REL as “the concentration level at or below which no health effects are anticipated in the general human population.” CalEPA's quantitative dose-response inform ation on carcinogenicity by inhalation exposure is expressed in terms of the URE [ 18 ], defined similarly to EPA's URE.

In developing chronic risk estimates, we adju sted dose-response values for some HAP based on professional judgment, as follows:

1) In the case of nickel compounds, to provide a health-protective estimate of potential cancer risks, we used the IRIS URE for nickel subsulfide in this assessment. Based on past scientific and technical considerations, the determination of the percent of nickel subsulfide was considered a major factor for estimating the extent and magnitude of the risks of cancer due to nickel-containing emissions. Nickel speciation information for some of the largest nickel-emitting sources (including oil combustion, coal combustion, and others) suggested that at least 35 percen t of total nickel emissions may be soluble compounds and that the URE for the mixture of inhaled nickel compounds (based on nickel subsulfide, and representative of pure insoluble crystalline nickel) could be derived to reflect the assumption that 65 percent of the total mass of nickel may be carcinogenic. Based on consistent views of major scientific bodies (i.e., National Toxicology Program (NTP) in their 12 th Report of the Carcinogens (ROC) [ 19 ], International Agency for Research on Cancer (IARC) [ 20 ], and other international agencies) [ 21 ] that consider all nickel compounds to be carcinogenic, we currently consider all nickel compounds to have the potential of being carcinogenic to humans. The 12th Report of the Carcinogens states that the “combined results of epidemiolo gical studies, mechanistic studies, and carcinogenic studies in rodents support the concept that nickel compounds generate nickelDraft Risk Assessment for the Petroleum Refining Source Sector – FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 16

ions in target cells at sites critical for car cinogenesis, thus allowing consideration and evaluation of these compounds as a single group.” Although the precise nickel compound (or compounds) responsible for the carcinogenic effects in humans is not always clear, studies indicate that nickel sulfate and the co mbinations of nickel sulfides and oxides encountered in the nickel refining industries cause cancer in humans (these studies are summarized in a review by Grimsrud et al., 2010 [ 22 ]). The major scientific bodies mentioned above have also recognized that there are differences in toxicity and/or carcinogenic potential across the different nickel compounds. In this inhalation risk assessment, to take a conservative approach, we have considered all nickel compounds to be as carcinogenic as nickel subsulfide and have applied the IRIS URE for nickel subsulfide without a factor to reflect the assumption that 100 percent of the total mass of nickel may be as carcinogenic as pure nickel subsulfide. In addition, given that there are two additional URE values 3 derived for exposure to mixtures of nickel compounds, as a group, that are 2-3 fold lower than the IRIS URE for nickel subsulfide, the EPA also considers it reasonable to use a value that is 50 percent of the IRIS URE for nickel subsulfide for providing an estimate of the lower end of the plausible range of cancer potency values for different mixtures of nickel compounds.

2) Where possible for emissions of unspecified mixtures of HAP categories such as metal compounds and POM, we apply category-specific chemical speciation profiles appropriate to the source category to develop a composite dose-response value for the category.

3) Where POM emissions were not speciated into individual compounds, we applied the same simplifying assumptio ns to assessments that are used in NATA [ 23 ]. The NATA approach partitions POM into eight different non-overlapping “groups” that are modeled as separate pollutants. Each POM group comprises POM species of similar carcinogenic potency, for which we can apply the same URE.

4) In the case of HAP categories such as glycol ethers and cyanide compounds, the most conservative dose-response value of the chemical category is used as a surrogate for other compounds in the group for which dose-response values are not available. This is done inorder to examine, under conservative assumptions, whether these HAP that lack doseresponse values may pose an unacceptable risk and require further examination, or screen out from further assessment.

5) In 2004, the EPA determined that the Chemi cal Industry Institute of Toxicology (CIIT) cancer dose-response value for formaldehyde (5.5 x 10 -9 per μ g/m 3 ) was based on better science than the IRIS cancer dose-response value (1.3 x 10 -5 per μ g/m 3 ), and we switched from using the IRIS value to the CIIT valu e in risk assessments supporting regulatory actions. Subsequent research published by EPA suggests that the CIIT model was not appropriate and, in 2010, EPA returned to using the 1991 IRIS value. The EPA has been

3 Two UREs (other than the current IRIS values) ha ve been derived for nickel compounds as a group: one developed by the California Department of Health Services (http://www.arb.ca.gov/toxics/id/summary/nickel_tech_b.pdf) and the other by the Texas Commission on Environmental Quality (http://www.epa.gov/ttn/a tw/nata1999/99pdfs/healtheffectsinfo.pdf).Draft Risk Assessment for the Petroleum Refining Source Sector – FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 17

working on revising the formaldehyde IRIS assessment and the National Academy of Sciences (NAS) completed its review of th e EPA’s draft assessment in April of 2011 4 The EPA will follow the NAS Report recommenda tions and will present results obtained by implementing the biologically-based dose-response (BBDR) model for formaldehyde. The EPA will compare these estimates with those currently presented in the External Review draft of the assessment and will discuss their strengths and weaknesses. As recommended by the NAS committee, appropriate sensitivity a nd uncertainty analyses will be an integral component of implementing the BBDR model. In the interim, we will present findings using the 1991 IRIS value as a primary estima te; EPA may also consider other information as the science evolves.

6) A chronic screening level of 163 μg/m3 was developed by EPA for carbonyl sulfide (COS) from a No Observed Adverse Effects Level (NOAEL) of 200 ppm based on brain lesions and neurophysiological alterations in rodents. A more detailed discussion of the studies used to develop the COS chronic screening level is provided in Appendix 8 of this document ( Development of the Chronic Scr eening Level for Carbonyl Sulfide ). The screening level includes a total uncertain ty factor (UF) of 3,000: 10 times for extrapolation for interspecies differences, 10 times for consideration of intraspecies variability, 10 times for extrapolation from subc hronic to chronic duration, and 3 times for database insufficiencies. See Section 5 of this document for a detailed discussion of exposure modeling uncertainties.

The emissions inventory for the petroleum refinery source sector includes emissions of 135 HAP with available chronic quantitative inhalation dose-response values. Of these, 69 are classified as known, probably, or possible car cinogens, with quantitative cancer dose-response values available. These 69 HAP, their quantitative inhalation chronic cancer dose-response values, and the source of each value are listed in Table 2.6-1.A. The 14 compounds with chronic oral cancer dose-response values available (for which multipathway screening assessments were performed) are listed in Table 2.6-1.B. Ninety-five HAP have quantitative inhalation chronic noncancer threshold values available; 7 of these 95 HAP, for which a multipathway assessment was performed, also have quantitative oral chronic noncancer threshold values available. These 95 HAP, their threshold values, and the source of the value are listed in Table 2.6-2.A and Table 2.6-2.B.

4 http://www.nap.edu/catalog.php?record_id=13142Draft Risk Assessment for the Petroleum Refining Source Sector – FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 18

Table 2.6-1 (A) Dose-Response Values for Chronic Inhalation Exposure to Carcinogens URE (unit risk esti mate for cancer) 5 = cancer risk per μ g/m 3 of average lifetime exposure. Sources: IRIS = EPA Integrated Risk Information System, EPA ORD = EPA Office of Research & Development, EPA OAQPS = EPA Office of Air Quality Planning & Standards, CAL = California EPA Office of Environmental Health Hazard Assessment. HEAST = EPA Health Effects Assessment Tables.

Pollutant CAS Number 6 URE 5 (1/ μ g/m 3 ) Source

1,1,2,2-Tetrachloroethane 79345 5.8E-05 CAL 1,1,2-Trichloroethane 79005 1.6E-05 IRIS 1,3-Butadiene 106990 3.0E-05 IRIS 1,3-Dichloropropene 542756 4.0E-06 IRIS 1,4-Dichlorobenzene 106467 1.1E-05 CAL 2-Nitropropane 79469 5.6E-06 EPA OAQPS 7
Acetaldehyde 75070 2.2E-06 IRIS Acrylamide 79061 1.6E-04 IRIS Acrylonitrile 107131 6.8E-05 IRIS
Aniline 62533 1.6E-06 CAL
Arsenic 7440382 4.3E-03 IRIS
Benzene 8 71432 7.8E-06 IRIS
Benzidine 92875 0.1072 IRIS Beryllium 7440417 2.4E-03 IRIS Bis(2-Ethylhexyl)Phthalate 117817 2.4E-06 CAL
Cadmium 7440439 1.8E-03 IRIS Chromium (VI) 18540299 1.2E-02 IRIS
Dioxins/Furans: 9
1,2,3,4,6,7,8- Heptachlorodibenzofuran 67562394 3.3E-01 EPA ORD1,2,3,4,6,7,8-Heptachlorodibenzop- d ioxin 35822469 3.3E-01 EPA ORD 1,2,3,4,7,8,9- Heptachlorodibenzofuran 55673897 3.3E-01 EPA ORD

5 The URE is the upper-bound excess cancer risk estimate d to result from continuous lifetime exposure to an agent at a concentration of 1 μ g/m 3 in air. URE’s are considered upper bound estimates meaning they represent a plausible upper limit to the true value.

6 Chemical Abstract Services identification number. Fo r groups of compounds that lack a CAS number we have used a surrogate 3-digit identifier corresponding to the group’s position on the CAA list of HAP.

7 URE of 5.6 x 10-6 per ug/m 3 for 2-nitropropane. This value was derived in 1999 by the Health Council of the Netherlands (available at : http://www.gr.nl/sites/defaul t/files/OSH@13.PDF), based on induction of hepatocellular nodules in rats, and is cons istent with weight-of-evidence determinations by the U.S. National Toxicology Program (“reasonably anticipated to be a human carcinogen”) and the IARC (“possibly carcinogenic to humans”).

8 The EPA IRIS assessment for benzene provides a range of plausible UREs. This assessment used the highest value in that range, 7.8E-06 per ug/m 3 . The low end of the range is 2.2E-06 per ug/m 3

. 9 Dioxins/Furans URE based on 2005 WHO evaluation of TEF (http://www.who.int/foodsafety/chem/tef_update/en/); URE=Group URE x TEF; RfC = 2,3,7,8-TCDD RfC/TEF Deleted: IRIS

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Table 2.6-1 (A) Dose-Response Values for Chronic Inhalation Exposure to Carcinogens URE (unit risk esti mate for cancer) 5 = cancer risk per μ g/m 3 of average lifetime exposure. Sources: IRIS = EPA Integrated Risk Information System, EPA ORD = EPA Office of Research & Development, EPA OAQPS = EPA Office of Air Quality Planning & Standards, CAL = California EPA Office of Environmental Health Hazard Assessment. HEAST = EPA Health Effects Assessment Tables.

Pollutant CAS Number 6 URE 5 (1/ μ g/m 3 ) Source

1,2,3,4,7,8- Hexachlorodibenzofuran 70648269 3.3E+00 EPA ORD1,2,3,4,7,8-Hexachlorodibenzo-pd ioxin 39227286 3.3E+00 EPA ORD 1,2,3,6,7,8- Hexachlorodibenzofuran 57117449 3.3E+00 EPA ORD1,2,3,6,7,8-Hexachlorodibenzo-pd ioxin 57653857 3.3E+00 EPA ORD 1,2,3,7,8,9- Hexachlorodibenzofuran 72918219 3.3E+00 EPA ORD1,2,3,7,8,9-Hexachlorodibenzo-pd ioxin 19408743 3.3E+00 EPA ORD 1,2,3,7,8- Pentachlorodibenzofuran 57117416 9.9E-01 EPA ORD1,2,3,7,8-Pentachlorodibenzo-pd ioxin 40321764 3.3E+01 EPA ORD 2,3,4,6,7,8- Hexachlorodibenzofuran 60851345 3.3E+00 EPA ORD 2,3,4,7,8- Pentachlorodibenzofuran 57117314 9.9E+00 EPA ORD 2,3,7,8-Tetrachlorodibenzofuran 51207319 3.3E+00 EPA ORD2,3,7,8-Tetrachlorodibenzo-pd ioxin 1746016 3.3E+01 EPA ORD 1,2,3,4,6,7,8,9- Octochlorodibenzofuran 39001020 9.9E-03 EPA ORD1,2,3,4,6,7,8,9-Octohlorodibenzop- d ioxin 3268879 9.9E-03 EPA ORD Ethyl Benzene 100414 2.5E-06 CAL Ethylene Dibromide 106934 6.0E-04 IRIS Ethylene Dichloride 107062 2.6E-05 IRIS 10
Ethylene Oxide 75218 8.8E-05 CAL Ethylidene Dichloride (1,1- Dichloroethane) 75343 1.6E-06 CAL Formaldehyde 11 50000 1.3E-05 IRIS Methyl Tert-Butyl Ether 1634044 2.6E-07 CAL Methylene Chloride 75092 1.3E-07 IRIS

10 The EPA IRIS assessment for ethylene dibromide provi des a range of plausible UREs. This assessment used the highest value in that range, 6.0E-04 per ug/m 3 . The low end of the range is equal to 50% of the URE for ethylene dibromide is 3.0E-04 per ug/m 3

. 11 The EPA has used the CIIT value (5.5x10 -9 per mg/m 3 ) to characterize formaldehyde cancer risks in some instances. Deleted: D

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Table 2.6-1 (A) Dose-Response Values for Chronic Inhalation Exposure to Carcinogens URE (unit risk esti mate for cancer) 5 = cancer risk per μ g/m 3 of average lifetime exposure. Sources: IRIS = EPA Integrated Risk Information System, EPA ORD = EPA Office of Research & Development, EPA OAQPS = EPA Office of Air Quality Planning & Standards, CAL = California EPA Office of Environmental Health Hazard Assessment. HEAST = EPA Health Effects Assessment Tables.

Pollutant CAS Number 6 URE 5 (1/ μ g/m 3 ) Source

Naphthalene 91203 3.4E-05 CAL
Nickel 7440020 4.8E-04 EPA OAQPS 12

Nickel Oxide 1313991 4.8E-04 EPA OAQPS 9
Polychlorinated Biphenyls 1336363 1.0E-04 IRIS Polycyclic Organic Matter 13 : 14
1-Methylnaphthalene 90120 8.8E-05 CAL 2-Methylnaphthalene 91576 8.8E-05 CAL 3-Methylcholanthrene 56495 1.008E-02 CAL 7,12-Dimethylbenz[a] a nthracene 57976 1.136E-01 CAL Acenaphthene 83329 8.8E-05 CAL Acenaphthylene 208968 8.8E-05 CAL Benz[a] a nthracene 56553 1.76E-04 CAL Benzo[a] p yrene 50328 1.76E-03 CAL Benzo[b] f luoranthene 205992 1.76E-04 CAL Benzo[e] p yrene 192972 8.8E-05 CAL Benzo[g,h,i,] p erylene 191242 8.8E-05 CAL Benzo[k] f luoranthene 207089 1.76E-04 CAL Chrysene 218019 1.76E-05 CAL Dibenzo[a,h] a nthracene 53703 1.918E-03 CAL Dibenzo[a,i] p yrene 189559 1.76E-02 CAL Dibenz[a,j] a cridine 224420 1.76E-04 CAL
Fluoranthene 206440 8.8E-05 CAL
Fluorene 86737 8.8E-05 CAL Indeno[1,2,3-c,d] p yrene 193395 1.76E-04 CAL Perylene 198550 8.8E-05 CAL
POM72002

- 8.8E-05 CAL,OAQPS 15

, Tetrachloroethene 127184 2.6E-07 IRIS Trichloroethylene 79016 4.8E-06 IRIS

12 The EPA IRIS assessments for nickel compounds provide a range of plausible UREs. This assessment used the highest value in that range which is equal to the URE for nickel subsulfide, 4.8E-04 per ug/m 3 . The low end of the range is equal to 50% of the URE for nickel subsulfide, 2.4E-04 per ug/m 3

. 13 POM without a chemical-specific URE are assigned a URE associated with a mixture of POM compounds having a similar characteristics. Details of this method, also used in the National Air Toxics Assessments, are available at http://www.epa.gov/ ttn/atw/nata2005/methods.html#pom
14 All POM act by a mutagenic mode-of-action, therefore the UREs have been adjusted by a factor of 1.6 (see section 2.7.1 for more details).

15 POM72002 represents unspecified POM. For more information on this group and the assigned URE see http://www.epa.gov/ttn/atw/nata1999/99pdfs/pomapproachjan.pdf Deleted: A

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Deleted: P

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Table 2.6-1 (A) Dose-Response Values for Chronic Inhalation Exposure to Carcinogens URE (unit risk esti mate for cancer) 5 = cancer risk per μ g/m 3 of average lifetime exposure. Sources: IRIS = EPA Integrated Risk Information System, EPA ORD = EPA Office of Research & Development, EPA OAQPS = EPA Office of Air Quality Planning & Standards, CAL = California EPA Office of Environmental Health Hazard Assessment. HEAST = EPA Health Effects Assessment Tables.

Pollutant CAS Number 6 URE 5 (1/ μ g/m 3 ) Source

Vinyl Bromide 593602 3.2E-05 HEAST Vinyl Chloride 75014 8.8E-06 IRIS

Table 2.6-1 (B) Dose-Response Values for Chronic Oral Exposure to Carcinogens

SF (oral slope factor for cancer) = cancer risk per mg/kg/d of average lifetime exposure. Sources: IRIS = EPA Integrated Risk Information System, CAL = Ca lifornia EPA Office of Environmental Health Hazard Assessment, EPA ORD = EPA Office of Research and Development

Pollutant CAS Number 16 SF (1/mg/kg/d) Source Dioxin/Furans: Hexachlorodibenzo-p-dioxin 19408743 6200 IRIS 2,3,7,8-Tetrachlorodibenzo-p-dioxin 1746016 150000 EPA ORD Polychlorinated biphenyls 1336363 2 IRIS Polycyclic Organic Matter: 3-Methlycholanthrene 56495 22 CAL 7,12-Dimethylbenz(a)anthracene 57976 250 CAL Benz[a]Anthracene 56553 1.2 CAL Benzo[a]Pyrene 50328 7.3 IRIS Benzo[b]Fluoranthene 205992 1.2 CAL Benzo[k]Fluoranthene 207089 1.2 CAL Chrysene 218019 0.12 CAL Dibenzo[a,h]Anthracene 53703 4.1 CAL Dibenzo(a,i)pyrene 189559 120 CAL Dibenz(a,j)acridine 224420 1.2 CAL Indeno[1,2,3-c,d]Pyrene 193395 1.2 CAL

16 Chemical Abstract Services identification number. For groups of compounds that lack a CAS number we have used a surrogate 3-digit identifier corresponding to the group’s position on the CAA list of HAP. Deleted: ¶Draft Risk Assessment for the Petroleum Refining Source Sector – FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 22

Table 2.6-2 (A) Dose-Response Values for Chronic Inhalation Exposure to Noncarcinogens

RfC (reference inhalation concentration) = an estimate (with uncertainty spanning perhaps an order of magnitude) of a continuous inhalation exposure to the human population (including sensitive subgroups) that is likely to be without an apprecia ble risk of deleterious effects during a lifetime. Sources: IRIS = EPA Integrated Risk Information System, CAL = California EPA Office of Environmental Human Health Assessment, ATSDR = US Agency for Toxic Substances Disease Registry, HEAST = EPA Health Effects Assessment Tables, EPA OAQPS = EPA Office of Air Quality Planning & Standards, EPA ORD = EPA Office of Research and Development
Pollutant CAS Number 6 RfC (mg/m 3 ) Source 17

1,1,2-Trichloroethane 79005 0.4 CAL 1,3-Butadiene 106990 0.002 IRIS - M 1,3-Dichloropropene 542756 0.02 IRIS - H 1,4-Dichlorobenzene 106467 0.8 IRIS - M 2-Nitroproprane 79469 0.02 IRIS - L Acetaldehyde 75070 0.009 IRIS - L
Acetonitrile 75058 0.06 IRIS - M
Acrolein 107028 0.00002 IRIS - M Acrylamide 79061 0.006 IRIS - M Acrylonitrile 107131 0.002 IRIS - M
Aniline 62533 0.001 IRIS - L Antimony 7440360 0.0002 IRIS - L
Arsenic 7440382 0.000015 CAL
Benzene 71432 0.03 IRIS - M Beryllium 7440417 0.00002 IRIS - M Bis(2-Ethylhexyl)Phthalate 117817 0.01 CAL
Cadmium 7440439 0.00001 ATSDR
Carbon Disulfide 75150 0.7 IRIS - M Carbonyl Sulfide 463581 0.163 EPA ORD 18

Chlorine 7782505 0.00015 ATSDR
Chlorobenzene 108907 1 CAL
Chloroform 67663 0.098 ATSDR Chromium (VI) 18540299 0.0001 IRIS - M
Cobalt 7440484 0.0001 ATSDR
Cobalt Oxide 1307966 0.0001 ATSDR Cresols (mixed) 1319773 0.6 CAL
m-Cresol 108394 0.6 CAL
o-Cresol 95487 0.6 CAL p-Cresol 106445 0.6 CAL
Cumene 98828 0.4 IRIS - M Hydrogen Cyanide 74908 0.0008 IRIS – L/M
Diethanolamine 111422 0.003 CAL

17 The descriptors L (low), M (medium), and H (high) ha ve been added for IRIS RfC values to indicate the overall level of confidence in the RfC value, as reported in the IRIS file.

18 A chronic screening level of 0.163 mg/m 3 was developed for carbonyl sulfide by EPA ORD from a No Observed Adverse Effects Level of 200 ppm based on brai n lesions and neurophysiological alteration in rodents.Draft Risk Assessment for the Petroleum Refining Source Sector – FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 23

Table 2.6-2 (A) Dose-Response Values for Chronic Inhalation Exposure to Noncarcinogens

RfC (reference inhalation concentration) = an estimate (with uncertainty spanning perhaps an order of magnitude) of a continuous inhalation exposure to the human population (including sensitive subgroups) that is likely to be without an apprecia ble risk of deleterious effects during a lifetime. Sources: IRIS = EPA Integrated Risk Information System, CAL = California EPA Office of Environmental Human Health Assessment, ATSDR = US Agency for Toxic Substances Disease Registry, HEAST = EPA Health Effects Assessment Tables, EPA OAQPS = EPA Office of Air Quality Planning & Standards, EPA ORD = EPA Office of Research and Development
Pollutant CAS Number 6 RfC (mg/m 3 ) Source 17

Dioxin/Furans: 1,2,3,4,6,7,8-Heptachlorodibenzofuran 67562394 0.000004 CAL 1,2,3,4,6,7,8-Heptachlorodibenzo-p-Dioxin 35822469 0.000004 CAL 1,2,3,4,7,8,9-Heptachlorodibenzofuran 55673897 0.000004 CAL 1,2,3,4,7,8-Hexachlorodibenzofuran 70648269 0.0000004 CAL 1,2,3,4,7,8-Hexachlorodibenzo-p-Dioxin 39227286 0.0000004 CAL 1,2,3,6,7,8-Hexachlorodibenzofuran 57117449 0.0000004 CAL 1,2,3,6,7,8-Hexachlorodibenzo-p-Dioxin 57653857 0.0000004 CAL 1,2,3,7,8,9-Hexachlorodibenzofuran 72918219 0.0000004 CAL 1,2,3,7,8,9-Hexachlorodibenzo-p-Dioxin 19408743 0.0000004 CAL 1,2,3,7,8-Pentachlorodibenzofuran 57117416 0.0000013 CAL 1,2,3,7,8-Pentachlorodibenzo-p-Dioxin 40321764 0.00000004 CAL 2,3,4,6,7,8-Hexachlorodibenzofuran 60851345 0.0000004 CAL 2,3,4,7,8-Pentachlorodibenzofuran 57117314 0.00000013 CAL 2,3,7,8-Tetrachlorodibenzofuran 51207319 0.0000004 CAL 2,3,7,8-Tetrachlorodibenzo-p-Dioxin 1746016 0.00000004 CAL 1,2,3,4,6,7,8,9-Octochlorodibenzofuran 39001020 0.00013 CAL 1,2,3,4,6,7,8,9-Octochlorodibenzo-p-Dioxin 3268879 0.00013 CAL Ethyl Chloride 75003 10 IRIS - M Ethyl Benzene 100414 1 IRIS - L Ethylene Dibromide 106934 0.009 IRIS - M Ethylene Dichloride 107062 2.4 ATSDR Ethylene Glycol 107211 0.4 CAL Ethylene Oxide 75218 0.03 CAL Ethylidene Dichloride (1,1-Dichloroethane) 75343 0.5 HEAST Formaldehyde 50000 0.0098 ATSDR Diethylene Glycol Monomethyl Ether 111773 0.02 IRIS - M
Hexachloroethane 67721 0.03 IRIS – L
Hexane 110543 0.7 IRIS – M Hydrochloric Acid 7647010 0.02 IRIS – L Hydrofluoric Acid 7664393 0.014 CAL
Lead 7439921 0.00015 EPA OAQPS Manganese 7439965 0.0003 ATSDR Elemental Gaseous Mercury 201 0.0003 IRIS - M Gaseous Divalent Mercury 184 0.0003 IRIS – M
Mercuric Chloride 7487947 0.0003 IRIS – M Mercury (elemental) 7439976 0.0003 IRIS – MDraft Risk Assessment for the Petroleum Refining Source Sector – FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 24

Table 2.6-2 (A) Dose-Response Values for Chronic Inhalation Exposure to Noncarcinogens

RfC (reference inhalation concentration) = an estimate (with uncertainty spanning perhaps an order of magnitude) of a continuous inhalation exposure to the human population (including sensitive subgroups) that is likely to be without an apprecia ble risk of deleterious effects during a lifetime. Sources: IRIS = EPA Integrated Risk Information System, CAL = California EPA Office of Environmental Human Health Assessment, ATSDR = US Agency for Toxic Substances Disease Registry, HEAST = EPA Health Effects Assessment Tables, EPA OAQPS = EPA Office of Air Quality Planning & Standards, EPA ORD = EPA Office of Research and Development
Pollutant CAS Number 6 RfC (mg/m 3 ) Source 17

Mercuric Oxide 21908532 0.0003 IRIS - M Particulate Divalent Mercury 202 0.0003 IRIS - M
Methanol 67561 4 CAL Methyl Bromide 74839 0.005 IRIS - H Methyl Chloride 74873 0.09 IRIS - M Methyl Isobutyl Ketone 108101 3 IRIS - L/M Methyl Tert-Butyl Ether 1634044 3 IRIS - M Methylene Chloride 75092 1 ATSDR Dimethyl formamide 68122 0.03 IRIS - M Naphthalene 91203 0.003 IRIS - M
Nickel 7440020 0.00009 ATSDR
Nickel Oxide 1313991 0.00002 CAL
Phenol 108952 0.2 CAL Propionaldehyde 123386 0.008 IRIS – L/M
Selenium 7782492 0.02 CAL Styrene 100425 1 IRIS - M
Tetrachloroethene 127184 0.04 IRIS – M
Toluene 108883 5 IRIS - H Trichloroethylene 79016 0.002 IRIS - H Triethylamine 121448 0.007 IRIS - L Vinyl Bromide 593602 0.003 IRIS - H Vinyl Chloride 75014 0.1 IRIS - M Vinylidene Chloride 75354 0.2 IRIS – H/M Xylenes (mixed) 1330207 0.1 IRIS - M m-Xylene 108383 0.1 IRIS - M o-Xylene 95476 0.1 IRIS - M p-Xylene 106423 0.1 IRIS - MDraft Risk Assessment for the Petroleum Refining Source Sector – FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 25

Table 2.6-2 (B) Dose-Response Values for Chronic Oral Exposure to Noncarcinogens

RfD (reference dose) = an estimate (with uncertainty spanning perhaps an order of magnitude) of a continuous oral exposure to the human population (inc luding sensitive subgroups) that is likely to be without an appreciable risk of deleterious effects during a lifetime. Sources: IRIS = EPA Integrated Risk Information System, ATSDR = US Agency for Toxic Substances and Disease Registry

Pollutant CAS Number RfD (mg/kg/d) Source 19
Cadmium compounds 7440439 0.0005 IRIS - H

Mercuric chloride 20 7439976 0.0003 IRIS - H Polycyclic Organic Matter (POM)

Anthracene 120127 0.3 IRIS - L

Fluoranthene 206440 0.04 IRIS - L 1-Methylnaphthalene 90120 0.07 ATSDR 2-Methylnaphthalene 91576 0.04 ATSDR 2,3,7,8-Tetrachlorodibenzo-p-dioxin 1746016 0.0000000007 IRIS - H

2.6.2 Sources of acute dose-response information Hazard identification and dose-response assessment information for preliminary acute inhalation exposure assessments are based on the existing recommendations of OAQPS for HAP [ 24 ]. Depending on availability, the results from screening acu te assessments are compared to both “no effects” reference levels for the general public, such as the California Reference Exposure Levels (RELs), as well as emergency response levels, such as Acute Exposure Guideline Levels (AEGLs) and Emergency Response Planning Guidelines (ERPGs), with the recognition that the ultimate in terpretation of any poten tial risks associated with an estimated exceedance of a particular reference level depends on the definition of that level and any limitations expressed therein. Comparisons among different available inhalation health effect reference values (both acute and chronic) for selected HAP can be found in recently released EPA documents [ 25 ] [ 26 ].

California Acute Reference Exposure Levels (RELs ). The California Environmental Protection Agency (CalEPA) has developed acute dose-response reference values for many substances, expressing the results as acute inhalation Reference Exposure Levels (RELs).

The acute REL ( http://www.oehha.ca.gov/air/pdf/acuterel.pdf ) is defined by CalEPA as “the concentration level at or below which no adverse health effects are anticipated for a specified exposure duration. [ 27 ]. RELs are based on the most sensitive, relevant, adverse health effect reported in the medi cal and toxicological literature. RELs are designed to protect the most sensitive indivi duals in the population by the inclusion of

19 The descriptors L (low), M (medium), and H (high) ha ve been added for IRIS RfC values to indicate the overall level of confidence in the RfC value, as reported in the IRIS file.

20 The multipathway exposure assessment for mercury in cluded fate and transport analysis, that included separate oral exposure estimates for divalent mercury and methylmercury. Formatted TableDraft Risk Assessment for the Petroleum Refining Source Sector – FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 26

margins of safety. Since margins of safety are incorporated to address data gaps and uncertainties, exceeding the REL does not automatically indicate an adverse health impact.” Acute RELs are developed for 1-hour (and 8-hour) exposures. The values incorporate uncertainty factors similar to those used in deriving EPA’s Inhalation Reference Concentrations (RfCs) for chronic exposures (and, in fact, California also has developed chronic RELs).

Acute Exposure Guideline Levels (AEGLs ). AEGLs are developed by the National Advisory Committee (NAC) on Acute Exposure Guide line Levels (NAC/AEGL) for Hazardous Substances, and then reviewed and published by the National Research Council. As described in the Committee’s “Standing Operating Procedures (SOP)” ( http://www.epa.gov/opptintr/aegl/pubs/sop.pdf ), AEGLs “represent thre shold exposure limits for the general public and are applicable to emergency exposures ranging from 10 min to 8 h.” Their intended application is “for conducting risk assessments to aid in the development of emergency preparedness and prevention plans, as well as real time emergency response actions, for accidental chemical releases at fixe d facilities and from transport carriers.” The document states that “the primary purpose of the AEGL program and the NAC/AEGL Committee is to develop guideline levels for once-in-a-lifetime, short-term exposures to airborne concentrations of acutely toxic, high -priority chemicals.” In detailing the intended application of AEGL values, the document states that, “It is anticipated that the AEGL values will be used for regulatory and nonregulatory purposes by U.S. Federal and State agencies, and possibly the international community in conjunction with chemical emergency response, planning, and prevention programs. More sp ecifically, the AEGL values will be used for conducting various risk assessments to aid in the development of emergency preparedness and prevention plans, as well as real-time emergency response actions, for accidental chemical releases at fixed facilities and from transport carriers.”

The NAC/AEGL defines AEGL-1 and AEGL-2 as:

“AEGL-1 is the airborne concentration (expressed as ppm or mg/m 3 ) of a substance above which it is predicted that the general popula tion, including susceptible individuals, could experience notable discomfort, irritation, or certain asymptomatic nonsensory effects. However, the effects are not disabling and are transient and reversible upon cessation of exposure.”

“AEGL-2 is the airborne concentration (expressed as ppm or mg/m 3 ) of a substance above which it is predicted that the general population, including susceptible individuals, could experience irreversible or other serious, long-lasting adverse health effects or an impaired ability to escape.”

“Airborne concentrations below AEGL-1 represent exposure levels that can produce mild and progressively increasing but transien t and nondisabling odor, taste, and sensory irritation or certain asymptomatic, nonsensory effects. With increasing airborne concentrations above each AEGL, there is a progressive increase in the likelihood of occurrence and the severity of effects described for each corresponding AEGL. Although the AEGL values represent threshold levels for the general public, including susceptibleDraft Risk Assessment for the Petroleum Refining Source Sector – FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 27

subpopulations, such as infants, children, the elderly, persons with asthma, and those with other illnesses, it is recognized that indi viduals, subject to unique or idiosyncratic responses, could experience the effects described at concentrations below the corresponding AEGL.”

Emergency Response Planning Guidelines (ERPGs). The American Industrial Hygiene Association (AIHA) has developed Emergency Response Planning Guidelines (ERPGs) [ 28 ] for acute exposures at three different levels of severity. These guidelines represent concentrations for once in a lifetime exposure of the general population (but not particularly sensitive persons) for up to 1 hour associated with effects expected to be mild or transient (ERPG-1), irreversible or serious (ERPG-2), and potentially life-threatening (ERPG-3).

ERPG values are described in their supporting documentation [ 29 ] as follows: “ERPGs are air concentration guidelines for single exposures to agents and are intended for use as tools to assess the adequacy of accident prevention and emergency response plans, including transportation emergency planning, community emergency response plans and incident prevention and mitigation.”

ERPG-1 and ERPG-2 values are defined by AIHA as follows:

“ERPG-1 is the maximum airborne concentratio n below which it is believed that nearly all individuals could be exposed for up to 1 hour without experiencing other than mild transient adverse health effects or without perceiving a clearly defined, objectionable odor.”

“ERPG-2 is the maximum airborne concentratio n below which it is believed that nearly all individuals could be exposed for up to 1 hour without experiencing or developing irreversible or other serious health effects or symptoms which could impair an individual's ability to take protective action.”

The emissions inventory for the petroleum refinery source sector includes emissions of 48 HAP with relevant and available quantitative acute dose-response threshold values. These HAP, their acute threshold values, and the source of the value are listed below in Table 2.6-3.

Table 2.6-3 Dose-Response Values for Acute Exposure

Pollutant CAS Number AEGL-1 (1-hr) (mg/m 3 ) AEGL-2 (1-hr) (mg/m 3 ) ERPG-1 (mg/m 3 ) ERPG-2 (mg/m 3 ) REL (mg/m 3 ) 1,3-Butadiene 106990 1500 12000 22 440 Acetaldehyde 75070 81 490 18 360 0.47
Acetonitrile 75058 22 540
Acrolein 107028 0.069 0.23 0.11 0.34 0.0025 Acrylonitrile 107131 10 130 22 77
Aniline 62533 30 46
Arsenic 7440382 0.0002
Benzene 71432 170 2600 160 480 1.3Draft Risk Assessment for the Petroleum Refining Source Sector – FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 28

Table 2.6-3 Dose-Response Values for Acute Exposure

Pollutant CAS Number AEGL-1 (1-hr) (mg/m 3 ) AEGL-2 (1-hr) (mg/m 3 ) ERPG-1 (mg/m 3 ) ERPG-2 (mg/m 3 ) REL (mg/m 3 ) Beryllium 7440417 0.025 Biphenyl 92524 61
Carbon Disulfide 75150 40 500 3.1 160 6.2 Carbonyl Sulfide 463581 140
Chlorine 7782505 1.5 5.8 2.9 8.7 0.21
Chlorobenzene 108907 46 690
Chloroform 67663 310 240 0.15
Cumene 98828 250 1500 Hydrogen Cyanide 74908 2.2 7.8 11 0.34 Ethyl Benzene 100414 140 4800 Ethylene Dibromide 106934 130 180 Ethylene Dichloride 107062 200 810 Ethylene Oxide 75218 81 90 Formaldehyde 50000 1.1 17 1.2 12 0.055 Diethylene Glycol Monomethyl Ether 111773 0.093
Hexane 110543 12000 Hydrochloric Acid 7647010 2.7 33 4.5 30 2.1 Hydrogen Fluoride 7664393 0.82 20 1.6 16 0.24 Elemental Gaseous Mercury 200 1.7 2 0.0006 Mercury (elemental) 7439976 1.7 2 0.0006
Methanol 67561 690 2700 260 1300 28 Methyl Bromide 74839 820 190 3.9 Methyl Chloride 74873 1900 830 Methyl Tert-Butyl Ether 1634044 180 2100 Methylene Chloride 75092 690 1900 1000 2600 14 Dimethyl formamide 68122 270 6 300
Nickel 7440020 0.0002
Nickel Oxide 1313991 0.0002
Phenol 108952 58 89 38 190 5.8 Propionaldehyde 123386 110 620 Styrene 100425 85 550 210 1100 21
Tetrachloroethene 127184 240 1600 680 1400 20
Toluene 108883 750 4500 190 1100 37 Trichloroethylene 79016 700 2400 540 2700 Triethylamine 121448 2.8 Vinyl Chloride 75014 640 3100 1300 13000 180 m-Xylene 108383 22 o-Xylene 95476 22 p-Xylene 106423 22 Xylenes (Mixture of o, m, and p Isomers) 1330207 560 4000 22Draft Risk Assessment for the Petroleum Refining Source Sector – FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 29



### 2.7 Risk characterization

2.7.1 General The final product of the risk assessment is the risk characterization, in which the information from the previous steps is integrated and an overa ll conclusion about risk is synthesized that is complete, informative, and useful for decision makers. In general, the nature of this risk characterization depends on the information available, the application of the risk information and the resources available. In all cases, major issues associated with determining the nature and extent of the risk are identified and discussed. Further, the EPA Administrator’s March
1995 Policy for Risk Characterization [ 30 ] specifies that a risk characterization “be prepared in a manner that is clear, transparent, reasonable, and consistent with other risk characterizations of similar scope prepared acro ss programs in the Agency.” These principles of transparency and consistency have been reinforced by the Agency’s Risk Characterization
Handbook [ 31 ], in 2002 by the Agency’s information quality guidelines [ 32 ], and in the OMB/OSTP September 2007 Memorandum on Updated Principles for Risk Analysis [ 33 ], and are incorporated in these assessments.

Estimates of health risk are presented in the c ontext of uncertainties and limitations in the data and methodology. Through our tiered, iterative analytical approach, we have attempted to reduce both uncertainty and bias to the greatest degree possible in these assessments, within the limitations of available time and resour ces. We provide summaries of risk metrics (including maximum individual cancer risks and noncancer hazards, as well as cancer incidence estimates) along with a discussion of the major uncertainties associated with their derivation to provide decision makers with the fullest picture of the assessment and its limitations.

For each carcinogenic HAP included in an assessment that has a potency estimate available, individual and population cancer risks were calculated by multiplying the corresponding lifetime average exposure estimate by the appr opriate URE. This calculated cancer risk is defined as the upper-bound probability of developing cancer over a 70-yr period (i.e., the assumed human lifespan) at that exposure. Because UREs for most HAP are upper-bound estimates, actual risks at a given exposure leve l may be lower than predicted, and could be

zero.

For EPA’s list of carcinogenic HAP that act by a mutagenic mode-of-action [ 34 ], we applied
EPA’s Supplemental Guidance for Assessing Susceptibility from Early-Life Exposure to Carcinogens [ 35 ]. This guidance has the effect of adjusting the URE by factors of 10 (for children aged 0-1), 3 (for children aged 2-15), or 1.6 (for 70 years of exposure beginning at birth), as needed in risk assessments. In th is case, this has the effect of increasing the estimated life time risks for these pollutants by a factor of 1.6. In addition, although only a small fraction of the total POM emissions may be reported as individual compounds, EPA expresses carcinogenic potency for compounds in this group in terms of benzo[a]pyrene equivalence, based on evidence that carcinogenic POM have the same mutagenic mechanism of action as does benzo[a]pyrene. For this reason, EPA implementation policy [ 36 ]Draft Risk Assessment for the Petroleum Refining Source Sector – FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 30

recommends applying the Supplemental Guidance to all carcinogenic PAHs for which risk estimates are based on relative potency. Accordingly, we applied the Supplemental Guidance to all unspeciated POM mixtures.

Increased cancer incidence for the entire receptor population within the area of analysis was estimated by multiplying the estimated lifetime cancer risk for each census block by the number of people residing in that block, then summing the results for the entire modeled domain. This lifetime population incidence estimate was divided by 70 years to obtain an estimate of the number of cancer cases per year.

In the case of benzene, the high end of the reported cancer URE range was used in our assessments to provide a conservative estimate of potential cancer risks. Use of the high end of the range provides risk estimates that are approximately 3.5 times higher than use of the equally plausible low end value. If the estimated benzene–associated risks exceed 1 in a million, we also evaluated the impact of usi ng the low end of the URE range on our risk results.

Unlike linear dose-response assessments for cancer, noncancer health hazards generally are not expressed as a probability of an adverse occurrence. Instead, “risk” for noncancer effects is expressed by comparing an exposure to a reference level as a ratio. The “hazard quotient” (HQ) is the estimated exposure divided by a reference level (e.g., the RfC). For a given HAP, exposures at or below the reference level (HQ ≤ 1) are not likely to cause adverse health effects. As exposures increase above the refe rence level (HQs increasingly greater than 1), the potential for adverse effects increases. For exposures predicted to be above the RfC, the risk characterization includes the degree of confidence ascribed to the RfC values for the compound(s) of concern (i.e., high, medium, or low confidence) and discusses the impact of this on possible health interpretations.

The risk characterization for chronic effects othe r than cancer is expressed in terms of the HQ for inhalation, calculated for each HAP at each census block centroid. As discussed above, RfCs incorporate generally conservative uncertainty factors in the face of uncertain extrapolations, such that an HQ greater than one does not necessarily suggest the onset of adverse effects. The HQ cannot be translated to a probability that adverse effects will occur, and is unlikely to be proportional to adve rse health effect outcomes in a population.

Screening for potentially significant acute inhalation exposures also followed the HQ approach. We divided the maximum estimated acute exposure by each available short-term threshold value to develop an array of HQ valu es relative to the various acute endpoints and thresholds. In general, when none of these HQ values are greater than one, there is no potential for acute risk. In those cases where HQ values above one are seen, additional information is used to determine if there is a potential for significant acute risks.Draft Risk Assessment for the Petroleum Refining Source Sector – FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 31

2.7.2 Mixtures Since most or all receptors in these assessmen ts receive exposures to multiple pollutants rather than a single pollutant, we estimated the aggregate health risks associated with all the exposures from a particular source category combined.

To combine risks across multiple carcinogens, our assessments use the mixtures guidelines’ [ 37

, 38 ] default assumption of additivity of effect s, and combine risks by summing them using the independence formula in the mixtures guidelines.

In assessing noncancer hazard from chronic exposures, in cases where different pollutants cause adverse health effects via completely different modes of action, it may be inappropriate to aggregate HQs. In consideration of these mode-of-action differences, the mixtures guidelines support aggregating effects of differ ent substances in specific and limited ways. To conform to these guidelines, we aggregated non-cancer HQs of HAP that act by similar toxic modes of action, or (where this information is absent) that affect the same target organ. This process creates, for each target organ, a target-organ-specific hazard index (TOSHI), defined as the sum of hazard quotients for individual HAP that affect the same organ or organ system. All TOSHI calculations presented here were based exclusively on effects occurring at the “critical dose” (i.e., the lowest dose th at produces adverse health effects). Although HQs associated with some pollutants have been aggregated into more than one TOSHI, this has been done only in cases where the critical dose affects more than one target organ. Because impacts on organs or systems that o ccur above the critical dose have not been included in the TOSHI calculations, some TOSH Is may have been underestimated. As with the HQ, the TOSHI should not be interpreted as a probability of adverse effects, or as strict delineation of “safe” and “unsafe” levels. Rather, the TOSHI is another measure of the potential for adverse health outcomes associated with pollutant exposure, and needs to be interpreted carefully by health scientists and risk managers.

Because of the conservative nature of the acute inhalation screening and the variable nature of emissions and potential exposures, acute impacts were screened on an individual pollutant basis, not using the TOSHI approach.

2.7.3 Facility-wide Risks To help place the source category risks in context, we examin ed “facility-wide” risks using the emissions data and modeling as described in Section 2.2. For the facilities in the petroleum refining source category, we estimated the maximum inhalation cancer and chronic noncancer risks associated with all HAP emissions sources at the facility, including emissions sources that are not part of the source categorie s but are located within a contiguous area and are under common control. We analyzed risks due to the inhalation of HAP for the populations residing within 50 kilometers of each facility. The results of the facility-wide assessment are summarized below in the risk ch aracterization section of this document.Draft Risk Assessment for the Petroleum Refining Source Sector – FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 32

2.7.4 MACT-Allowable Risk The emissions data in the data set for the petroleum refining source category are estimates of actual emissions on an annual basis. For the petroleum refining source category, allowable emissions were also estimated and inhalation assessment results obtained. The docket memo Refinery Emissions and Risk Estimates for Modeled “Allowable” Emissions provides a full explanation of the development of these allowable emissions.as is described in Appendix 9[ 39 ]



### 3 Risk results for the petroleum refining source sector

3.1 Source category emissions and results
The petroleum refining source sector consists of 142 facilities regulated under the refinery MACT 1 and MACT 2 standards. Petroleum refi neries are facilities that engage in producing liquid petroleum gases (LPG), motor gasoline, jet fuels, kerosene, distillate fuel oils, residual fuel oils, lubricants, asphalt (bitumen), and ot her products through distillation of crude oil or through redistillation, cracking, or reforming of unfinished petroleum derivatives. The first and primary source category for which regulations were developed, Petroleum Refineries – Other Sources Not Distinctly Listed (Refiner y MACT 1), includes all emission sources from petroleum refinery process units except those that were expected to be regulated elsewhere, such as the NESHAP for Boilers and Process Heaters (40 CFR part 63 subpart DDDDD). The Refinery MACT 1 rule, which focuses on evaporative emissions, was published in 40 CFR part 63, subpart CC on August 18, 1995 (6 0 FR 43620) and specifically excludes three types of process vents: catalytic cracking unit catalyst regeneration vents, catalytic reforming unit catalyst regeneration vents, and sulfur plant vents. These specific vents are regulated by the NESHAP for Petroleum Refineries: Cataly tic Cracking Units, Catalytic Reforming Units, and Sulfur Recovery Units (Refinery MACT 2) in 40 CFR part 63, subpart UUU. The Refinery MACT 2 standards, which focus on combustion sources, were promulgated on April 11, 2002 (67 FR 17762). This analysis will ev aluate the risks associated with both the MACT 1 and MACT 2 emissions.

We currently estimate that of the 148 Petroleum Refinery facilities operating in the U.S., that 142 of these are identified as major sources and 6 identified as area sources. Only the major sources are included in this assessment.

The emissions for this source category data set were obtained from the ICR, as mentioned above [ 5 ]. The emissions from the source category are summarized in Table 3.1-1. The total HAP emissions for the source category are approximately 17,200 tons per year. Based on these data, the HAP emitted in the largest quan tities across these 142 facilities are: hydrogen cyanide, n-hexane, toluene, xylenes (mixed), benzene, 2,2,4-trimethylpentane, hydrochloric acid, hydrogen sulfide, ethyl benzene, methanol, carbonyl sulfide, styrene, naphthalene, formaldehyde, cumene, methyl isobutyl ketone, p-xylene, carbon disulfide, 1,3-butadiene, methyl tert-butyl ether, m-xylene, o-xylene, hydrofluoric acid, 2-methylnapthalene, phenol, cresols (mixed), diethanolamine, acetaldehyde , methylene chloride, biphenyl, ethylene dibromide, and tetrachloroethene. These 32 HAP account for 99 percent of the total emissionsDraft Risk Assessment for the Petroleum Refining Source Sector – FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 33

by mass. Persistent and bioaccumulative HAP (PB HAP) 21 reported as emissions from these facilities include cadmium compounds, chlori nated dibenzodioxins and furans (analyzed as 2,3,7,8-tetrachlorodibenzo-p-dioxin), mercury compounds, lead compounds, polychrorinated biphenyls (PCBs), and polycyclic organic matter (POM) (analyzed as benzo(a)pyrene). The following environmental HAP are emitted from th e petroleum refineries source category and are included in the environmental risk screen: lead compounds, mercury compounds, cadmium compounds, POM, chlorinated dibenzodioxins and furans, hydrogen chloride, and hydrogen fluoride.

Table 3.1-1. Summary of Emissions from the Petroleum Refinery Source Category and Availability of Dose-Response Values

HAP a Emissions (tpy) Number of Facilities Reporting HAP (148 facilities in

NPRM data set) Prioritized Inhalation Dose-Response Value Identified by OAQPS b

PBHAP? Unit Risk Estimate for Cancer? Reference Concentration

for Chronic Noncancer? Health
Benchmark Values for

Acute Noncancer? Hydrogen Cyanide 3,682 80 

 

 n-Hexane 2,513 142 

 

 Toluene 2,284 142 

 

 Xylenes (mixed) 1,840 139 

 

 Benzene 1,174 142 

 

 

 2,2,4-Trimethylpentane 1,086 129 Hydrochloric Acid 931 98 

 

 Ethyl Benzene 491 141 

 

 

 Methanol 466 66 

 

 Carbonyl Sulfide 366 82 

 

 Styrene 345 114 

 

 Naphthalene 265 140 

 

 Formaldehyde 196 119 

 

 

 Cumene 163 135 

 

 Methyl Isobutyl Ketone 128 21 

 Carbon Disulfide 122 100 

 

 p-Xylene 122 51 

 

 Methyl Tert-butyl Ether 116 76 

 

 

 1,3-butadiene 113 125 

 

 

 m-Xylene 108 74 

 

 o-Xylene 66 65 

 

 Hydrofluoric Acid 63 24 

 

 Polycyclic Organic Matter

 

21 Persistent and bioaccumulative HAP are defined in the EPA’s Air Toxics Risk Assessment Library , Volume 1, EPA-453K-04-001A, as referenced in the NPRM and provided on the EPA’s Technology Transfer Network website for Fate, Exposure, and Risk Assessment at http://www.epa.gov/ttn/fera/risk_atra_vol1.html

. Formatted Table

Formatted TableDraft Risk Assessment for the Petroleum Refining Source Sector – FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 34

Table 3.1-1. Summary of Emissions from the Petroleum Refinery Source Category and Availability of Dose-Response Values

HAP a Emissions (tpy) Number of Facilities Reporting HAP (148 facilities in

NPRM data set) Prioritized Inhalation Dose-Response Value Identified by OAQPS b

PBHAP? Unit Risk Estimate for Cancer? Reference Concentration

for Chronic Noncancer? Health
Benchmark Values for

Acute Noncancer? 2-methylnaphthalene 60 106 

 

 Anthracene 16 110 

 Phenanthrene 14 109 

 Fluorene 8 104 

 

 Pyrene 3 101 

 Chrysene 3 106 

 

 Acenaphthene 2 103 

 

 Fluoranthene 2 104 

 

 POM 72002 1 5 

 

 Benzo(ghi)perylene 0.8 110 

 

 Benz[a]anthracene 0.6 106 

 

 Perylene 0.6 14 

 

 Benzo[a]pyrene 0.5 105 

 

 Benzo(e)pyrene 0.5 84 

 

 Indeno[1,2,3-c,d]pyrene 0.5 104 

 

 1-methylnaphthalene 0.4 1 

 

 Dibenzo[a,h]anthracene 0.2 106 

 

 Benzo[b]fluoranthene 0.2 105 

 

 Acenaphthylene 0.2 104 

 

 Benzo[k]fluoranthene 0.1 105 

 

 7,12-dimethylbenz[a]anthracene 0.02 61 

 

 3-methylcholanthrene 0.01 61 

 

 Dibenzo[a,i]pyrene 0.0002 1 

 

 Dibenz[a,j]acridine 0.0001 1 

 

 Phenol 59 117 

 

 Cresols (mixed) 58 97 

 Diethanolamine 49 31 

 Acetaldehyde 47 107 

 

 

 Methylene Chloride 43 8 

 

 

 Biphenyl 41 116 

 Ethylene Dibromide 27 12 

 

 

 Tetrachloroethene 22 31 

 

 

 2-nitropropane 20 2 

 

 Acetonitrile 12 4 

 

 Chlorine 10 76 

 

 Nickel Compounds

  Nickel Compounds 9 103 

 

 

 Nickel Oxide 0.01 1 

 

 

 Formatted TableDraft Risk Assessment for the Petroleum Refining Source Sector – FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 35

Table 3.1-1. Summary of Emissions from the Petroleum Refinery Source Category and Availability of Dose-Response Values

HAP a Emissions (tpy) Number of Facilities Reporting HAP (148 facilities in

NPRM data set) Prioritized Inhalation Dose-Response Value Identified by OAQPS b

PBHAP? Unit Risk Estimate for Cancer? Reference Concentration

for Chronic Noncancer? Health
Benchmark Values for

Acute Noncancer? Acrylonitrile 9 2 

 

 

 Aniline 7 44 

 

 

 Acrolein 6 104 

 

 Chlorobenzene 5 3 

 

 Antimony Compounds 5 97 

 Trichloroethylene 5 11 

 

 

 1,4-dichlorobenzene 3 39 

 

 o-Cresol 3 24 

 Ethylene Dichloride 3 13 

 

 

 Methyl Bromide 2 57 

 

 1,3-dichloropropene 2 2 

 

 Selenium Compounds 2 99 

 Lead Compounds 1 101 c  Chromium Compounds

 Chromium (III) 1 98 Chromium (VI) 0.2 101 

 

 1,1,2-trichloroethane 1 7 

 

 Manganese Compounds 1 97 

 Phosphorus, White 1 41 m-Cresol (3-methylphenol) 1 21 

 Methyl Chloride 1 5 

 

 Chloroform 0.9 15 

 

 Vinyl Chloride 0.7 3 

 

 

 1,1,2,2-tetrachloroethane 0.7 6 

 Ethyl Chloride 0.7 2 

 Mercury Compounds

   Mercury (elemental) 0.6 106 

 

 
Gaseous Divalent Mercury 0.08 106 

 

 Particulate Divalent Mercury 0.08 106 

 

 Mercuric Chloride 0.009 3 

 

 Mercuric Oxide 0.0001 1 

 

 Ethylidene Dichloride 0.6 2 

 

 Cadmium Compounds 0.5 97 

 

 

 Cobalt Compounds

  Cobalt Compounds 0.4 95 

 Cobalt Oxide 0.007 1 

 Arsenic Compounds 0.2 96 

 

 

 p-Cresol (4-methy Phenol) 0.2 14 

 Formatted TableDraft Risk Assessment for the Petroleum Refining Source Sector – FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 36

Table 3.1-1. Summary of Emissions from the Petroleum Refinery Source Category and Availability of Dose-Response Values

HAP a Emissions (tpy) Number of Facilities Reporting HAP (148 facilities in

NPRM data set) Prioritized Inhalation Dose-Response Value Identified by OAQPS b

PBHAP? Unit Risk Estimate for Cancer? Reference Concentration

for Chronic Noncancer? Health
Benchmark Values for

Acute Noncancer? Bis(2-ethylhexyl)phthalate 0.2 7 

 

 Dibenzofuran 0.2 21
Nitrobenzene 0.2 1   Ethylene Oxide 0.1 1 

 

 

 Dibutylphthalate 0.1 5
Hexachloroethane 0.1 1 

 Beryllium Compounds 0.08 94 

 

 

 Triethylamine 0.06 2 

 

 Vinyl Bromide 0.03 1 

 

 Quinoline 0.02 2 Dimethyl Formamide 0.005 1 

 

 Acrylamide 0.004 2 

 

 Vinylidene Chloride 0.004 1 

 Benzidine 0.002 1 

 

 Diethylene Glycol Monomethyl Ether 0.001 1 

 

 Polychlorinated Biphenyls 0.001 57 

  3,3'-dimethylbenzidine 0.0009 1

  3,3'-dimethoxybenzidine 0.0004 1

  N,N-dimethylaniline 0.0004 1

  p-Phenylenediamine 0.0004 1

  Propionaldehyde 0.0004 3 

 

 Ethylene Glycol 0.0001 1 

 o-Toluidine 0.00009 1 

  Isophorone 0.00006 1

 

 Dioxins and Furans

 2,3,7,8-tetrachlorodibenzo-p-dioxin 0.000002 52 

 

 

 1,2,3,4,6,7,8-heptachlorodibenzofuran 0.000002 16 

 

 

 1,2,3,4,6,7,8-heptachlorodibenzo-p-dioxin 0.000001 61 

 

 

 1,2,3,6,7,8-hexachlorodibenzofuran 0.0000009 54 

 

 

 1,2,3,4,6,7,8,9-octochlorodibenzo-p-dioxin 0.0000006 18 

 

 

 1,2,3,4,7,8-hexachlorodibenzofuran 0.0000005 19 

 

 

 2,3,4,7,8-pentachlorodibenzofuran 0.0000005 53 

 

 

 1,2,3,4,6,7,8,9-octochlorodibenzofuran 0.0000004 13 

 

 

 2,3,4,6,7,8-hexachlorodibenzofuran 0.0000003 13 

 

 

 1,2,3,7,8-pentachlorodibenzofuran 0.0000002 17 

 

 

 1,2,3,4,7,8,9-heptachlorodibenzofuran 0.0000001 14 

 

 

 2,3,7,8-tetrachlorodibenzofuran 0.00000009 15 

 

 

 1,2,3,7,8,9-hexachlorodibenzofuran 0.00000008 12 

 

 

 Formatted TableDraft Risk Assessment for the Petroleum Refining Source Sector – FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 37

Table 3.1-1. Summary of Emissions from the Petroleum Refinery Source Category and Availability of Dose-Response Values

HAP a Emissions (tpy) Number of Facilities Reporting HAP (148 facilities in

NPRM data set) Prioritized Inhalation Dose-Response Value Identified by OAQPS b

PBHAP? Unit Risk Estimate for Cancer? Reference Concentration

for Chronic Noncancer? Health
Benchmark Values for

Acute Noncancer? 1,2,3,6,7,8-hexachlorodibenzo-p-dioxin 0.00000008 11 

 

 

 1,2,3,7,8-pentachlorodibenzo-p-dioxin 0.00000007 15 

 

 

 1,2,3,7,8,9-hexachlorodibenzo-p-dioxin 0.00000005 11 

 

 

 1,2,3,4,7,8-hexachlorodibenzo-p-dioxin 0.00000003 13 

 

 



a Notes for how HAP were speciated for risk assessment:

 For most metals, emissions reported as the elemental metal are combined with metal compound emissions (e.g., “cadmium” emissions modeled as “cadmium & compounds”). In the absence of speciation information, we assume the reported mass is 100 percent metal.

 For emissions of certain chemicals or chemical groups classi fied as POM, some (not that are not already speciated) emissions were grouped into POM subgroups as found on EPA’s Technology Transfer Network website for the 2005 National-Scale Air Toxics Assessment at http://www.epa.gov/ttn/atw /nata2005/methods.html#pom ( Approach to Modeling POM)

. These POM subgroups (e.g., POM 72002) all have unit risk estimates for cancer, and are considered PB-HAP. Pollutants previously grouped under POM 73002, POM 74002, POM 75002, and POM 76002 are listed individually in this table.

 For emissions reported generically as “Glycol Ethers” or specific glycol ethers not found on EPA’s Technology Transfer network for air toxics (see footnote b), emissions will be treated as ethylene glycol methyl ether.

 For emissions reported generically as “chromium” or “chromium & compound,” emissions are speciated “chromium (III) compounds” and “chromium (VI) compounds” according to the individual emitting process speciation profile for this source category. Chromium speciation profiles can be found on the EPA’s Technology

Transfer Network website for emissions inventories at http://www.epa.gov/ttn/chief/net/2005inventory.html

.

 For emissions reported generically as “mercury” or “mer cury & compounds,” emissions are speciated for this category as 80% “mercury (elemental)”, 10% “gaseous divalent mercury”, and 10% “particulate divalent mercury”. Mercury speciation profiles can be found on the EPA’s Technology Transfer Network website for emissions inventories at http://www.epa.gov/ttn/chief/net/2005inventory.html

.

b Specific dose-response values for each chemical are identi fied on EPA’s Technology Transfer Network website for air toxics at http://www.epa.gov/ttn/atw/toxsource/summary.html

c There is no reference concentration for lead. In considering noncancer hazards for lead in this assessment, we compared maximum annual ambient exposure estimates to the Nationa l Ambient Air Quality Standards (NAAQS) for lead (0.15 μg/m 3 ). These NAAQS for lead were reviewed with revisions adopted in October 2008 ( http://www.epa.gov/air/lead/actions.html ). The primary (health-based) standard is a maximum or not-to-be-exceeded, rolling three-month average, measured as total suspended particles (TSP ). The secondary (welfare-based) standard is identical to the primary standard.



### 3.2 Baseline risk characterization This section presents the results of the baseline risk assessment for the petroleum refining source sector based on the modeling methods described in the previous sections. All baseline risk results are developed using the best estimates of actual HAP emissions summarized in the previous section. The basic chronic inhalation risk estimates presented here are the maximum individual lifetime cancer risk, the maximum chro nic hazard index, and the cancer incidence. Formatted TableDraft Risk Assessment for the Petroleum Refining Source Sector – FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 38

We also present results from our acute inhalation impact screening assessment in the form ofmaximum hazard quotients, as well as the results of our preliminary screen for potential noninhalation risks from PB-HAP. Also presente d are the HAP “drivers,” which are the HAP that collectively contribute 90 percent of the maximum cancer risk or maximum hazard at the highest exposure location, as well as our anal ysis of risks associated with the maximum allowed emissions under the current MACT standard s. Definitions of these and other relevant risk-related terms can be found on the 2005 National-Scale Air Toxics Assessment Glossary Web page . A detailed summary of the facility-specific risk assessment results is available in Appendix 5 of this document ( Detailed Risk Modeling Results ).

MACT-level Inhalation Assessment Results

Tables 3.2-1 and 3.2-2 summarize the chronic and acute inhalation risk results for this source category. The results of the chronic inhalati on cancer risk assessment are that the maximum lifetime individual cancer risk posed by the 142 facilities could be 60 in a million considering both the maximum facility, and the entire source sector. Emissions of naphthalene and benzene from process equipment leaks and storage tanks are the major contributors to the risk. The total estimated cancer incidence from this source category is 0.3 excess cancer cases per year, or one excess case in every 3 years. Approximately 100,000 people were estimated to have cancer risks above 10 in a million and approximately 5,000,000 people were estimated to have cancer risks above 1 in a million considering the 142 facilities in this source category. The maximum chronic noncancer TOSHI value for the source category could be up to 0.9 driven by emissions of hydrogen cyanide from catalytic cracking unit vents.

Analysis of potential differences between actu al emissions levels and the maximum emissions allowable under the MACT standards (i.e., MACT -allowable emissions) were also calculated for the source category. MACT-allowable emi ssions were estimated by using two different methods, a model plant approach (Refinery Emissions Model (REM), approach) and an approach that combined the results of the actual and REM emissions estimates to develop a more conservative allowable risk estimate.

Appendix 9 provides a memo that describes in de tails these two approaches as well as the risk estimates using from both approaches. Risk results from the inhalation risk assessment using the MACT-allowable emissions indicate that the maximum lifetime individual cancer risk could be higher than those from the actual analysis, or 90 to 100 in a million, and that the maximum chronic noncancer Hazard Index value is about 1. The total estimated cancer incidence from this source category considering allowable emissions is expected to range between 0.4 and 0.6 excess cancer cases per year or one excess case in every 1.5 to 2.5 years. The REM model approach predicts that approximately 90,000 people were estimated to have cancer risks above 10 in a million and approximately 7,000,000 people were estimated to have cancer risks above 1 22 . Chronic noncancer risks from the MACT-allowable emissions are expected to remain at or below 1.

22 Population risks from allowable emissions were only calculated for the model plant emissions (REM) approach. For the 138 facilities modeled using the modeled plant approach the population risks greater than 10-in-1Draft Risk Assessment for the Petroleum Refining Source Sector – FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 39

Worst-case acute hazard quotients (HQs) were calculated for every HAP shown in Table 3.1-1 that has an acute benchmark, and the highest refined screening acute HQ value of 5 (based on the acute REL for nickel) is shown in Table 3.2-1. This value includes a refinement of determining the highest HQ value th at is outside facility boundaries. It’s also important to note that the HQ of 5 for Nickel assumes that the primary source of the nickel emissions, the fluid catalytic cracking unit ve nt, was modeled with an hourly emissions multiplier of 60 times the annual emissions ra te. There are also refined acute HQ values (based on RELs) greater than 1 for acetaldehyde, acrolein, arsenic compounds, benzene, and nickel compounds. In all, six facilities are estimated to have a refined acute HQ (based on an REL) grea ter than 1 (four of these facilities are 2 or less). No facilities are estimated to have a AE GL-1 greater than 1. Acute estimates for each plant and pollutant are provided in Appendix 5 of this document, while refined estimates are provided in Appendix 6 of this document ( Acute Impacts Refined Analysis ).

Table 3.2-1. Summary of Source Category Level Risks for Petroleum Refineries

Result HAP “Drivers” Facilities in Source Category
Number of Facilities Estimated to be in Source Category 142 n/a

Number of Facilities Identified by ICR and Modeled in Risk Assessment 142 n/a

Cancer Risks
Maximum Individual Lifetime Cancer Risk (in 1 million) 60 naphthalene, benzene

Number of Facilities with Maximum Individual Lifetime Cancer Risk: Greater than or equal to 100 in 1 million 0 n/a

Greater than or equal to 10 in 1 million 48 naphthalene, benzene, ethyl benzene, ethylene dibromide, 7,12- dimethylbenz[a]anthracene, benzidine, dibenzo[a,h]anthracen e, benzo[a]pyrene, 1,1,2-trichloroethane, 2- methylnaphthalene, acetaldehyde, POM 72002, fluorene, benzo[b]fluoranthene, formaldehyde, 3-methylcholanthrene,

acrylonitrile, nickel compounds, 1-methylnaphthalene, benz[a]anthracene, indeno[1,2,3-c,d]pyren e, chrysene, arsenic compounds, tetrachloroethene, cadmium

million was estimated to be 90,000. If we consider the second approach to determining allowable emissions (combined the results of the actual and REM emissions estimates) we estimate that the allowable population risks greater than 10-in-1 million would be greater than 90,000 people. Further, the number of people above 1-in-1 million would also be higher than the 7,000,000 estimated using the REM model. Formatted TableDraft Risk Assessment for the Petroleum Refining Source Sector – FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 40

Table 3.2-1. Summary of Source Category Level Risks for Petroleum Refineries

Result HAP “Drivers” compounds, fluoranthene, benzo(ghi)perylene, chromium (VI) compounds, 1,3-butadiene, 1,1,2,2- tetrachloroethane, ethylene dichloride, 2-

nitropropane, acenaphthene, berylliumcompounds, 2,3,7,8-tetrachlorodibenzo-pdioxin, methyl tert-butyl ether, benzo(e)pyrene

Greater than or equal to 1 in 1 million 111 naphthalene, benzene, ethyl benzene, ethylene dibromide, 7,12- dimethylbenz[a]anthracene, benzidine, dibenzo[a,h]anthracen e, benzo[a]pyrene, 1,1,2-trichloroethane, 2- methylnaphthalene, acetaldehyde, POM 72002, fluorene, benzo[b]fluoranthene, formaldehyde, 3-methylcholanthrene,

acrylonitrile, nickel compounds, 1-methylnaphthalene, benz[a]anthracene, indeno[1,2,3-c,d]pyren e, chrysene, arsenic compounds, tetrachloroethene, cadmium compounds, fluoranthene, benzo(ghi)perylene, chromium (VI) compounds, 1,3-butadiene, 1,1,2,2- tetrachloroethane, ethylene dichloride, 2-

nitropropane, acenaphthene, berylliumcompounds, 2,3,7,8-tetrachlorodibenzo-pdioxin, methyl tert-butyl ether,benzo(e)pyrene, ethylene oxide, pdichlorobenzene, vinyl chloride, 1,3-

dichloropropene, perylene, aniline Chronic Noncancer Risks Maximum Neurological Hazard Index 0.9 hydrogen cyanide Number of Facilities with Maximum Neurological Hazard Index:
Greater than 1 0 n/a Acute Noncancer Screening Results

Maximum Acute Hazard Quotient 23 5
2
2
2
2 nickel compounds (REL) arsenic compounds (REL) acrolein (REL) benzene (REL) acetaldehyde (REL)
Number of Facilities With Potential for Acute Effects 6 nickel compounds, arsenic compounds, acrolein, benzene, acetaldehyde Population Exposure Number of People Living Within 50 Kilometers of Facilities Modeled 83,000,000 n/a

Number of People Exposed to Cancer Risk:

23 As shown in Tables 2.6.3 and 3.2-2 there are no AEGL or ERPG values for nickel or arsenic compounds. Additionally, there are no short-term occupational values for these metals to compare to the REL values. Formatted Table

Deleted: yDraft Risk Assessment for the Petroleum Refining Source Sector – FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 41

Table 3.2-1. Summary of Source Category Level Risks for Petroleum Refineries

Result HAP “Drivers” Greater than or equal to 100 in 1 million 0 n/a

Greater than or equal to 10 in 1 million 100,000 n/a

Greater than or equal to 1 in 1 million 5,000,000 n/a

Number of People Exposed to Noncancer Neurological Hazard Index:
Greater than 1 0 n/a Estimated Cancer Incidence (excess cancer cases per year) 0.3 n/a

Contribution of HAP to Cancer Incidence naphthalene 22% n/a
benzene 21% n/a 2-methylnaphthalene 13% n/a 7-12-dimethylbenz[a]anthracene 10% n/a 1,3-butadiene 6% n/a nickel compounds 5% n/a ethylene dibromide 5% n/a ethyl benzene 4% n/a benzo[a]pyrene 2% n/a

formaldehyde 2% n/a Contribution of Process to Cancer Incidence Coking unit vents & coke handling 31% n/a Process equipment leaks 23% n/a
Wastewater treatment 12% n/a Catalytic cracking unit vent 10% n/a Storage tanks 7% n/a Cooling towers 6% n/a
Flares 5% n/a Marine vessel loading & unloading 2% n/a Catalytic reforming unit vents 1% n/a Refinery processes (other, general) 1% n/a
Incinerators 1% n/a Formatted TableDraft Risk Assessment for the Petroleum Refining Source Sector – FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 42

Table 3.2-2. Summary of Refined Acute Results for Petroleum Refining Source Sector

Refined Results Maximum Acute Hazard Quotients Acute Dose-Response Values

HAP Max. 1- hr. Air Conc. (mg/m 3 ) Based on
REL Based on AEGL-1/ ERPG-1 Based on AEGL-2/ ERPG-2 REL (mg/m 3 ) AEGL-1 (1-hr) (mg/m 3 ) ERPG-1 (mg/m 3 ) AEGL-2 (1-hr) (mg/m 3 ) ERPG-2 (mg/m 3 )

Acetaldehyde 1.0 2 0.05 .003 0.47 81 18 490 360

Acrolein 0.005 2 0.1 0.03 0.0025 0.069 0.11 0.23 0.34
Arsenic compounds 0.0004 2 na na 0.0002 na na na na

Benzene 2.7 2 0.02 0.006 1.3 170 160 2600 480
Nickel compounds 0.001 5 na na 0.002 na na na na

Notes on Refined Process: 1) The initial screening was performed for all emitted HAP with ava ilable acute dose-response values. Only those pollutants whose screening HQs were greater than 1 for at least one acute threshold value are shown in the table. 2) HAP with available acute dose-response values which are not in the table do not carry any potential for posing acute health ris ks, based on an analysis of currently available emissions data.

Notes on Acute Dose-Response Values: REL – California EPA reference exposure level for no adverse ef fects. Most, but not all, R ELs are for 1-hour exposures. AEGL – Acute exposure guideline levels represent emergency exposure (1-hour) limits for the general public. AEGL-1 is the exposure level above which it is predicted that the general population, including susceptible individuals, could experience effects that are notable discomfort, but which are transient and reversible upon cessation of exposure. AEGL-2 is the exposure level above which it is predicted that the general population, including susceptible individuals, could experience irreversible or other serious, long-lasting adverse health effects of an impaired ability to escape. EPRG – Emergency Removal Program guidelines represent emergency exposure (1-hour) limits for the general public. ERPG-1 is the maximum level below which it is believed that nearly all individuals could be exposed for up to 1 hour without experiencing other than mild, transient adverse health effects. ERPG-2 is the maximum exposure below which it is believed that nearly all individuals could be exposed for up to 1 hour without experiencing or developing irreversible or other serious health effects or symptoms which could impair an individual’s ability to take protective action.

MACT-level Multipathway Assessment Results

To identify potential multipathway health risk s from PB-HAP other than lead, we first performed a screening analysis (Tier I and II) that compared emissions of PB-HAP emitted from the petroleum refining source sector (based on actual emissions) to screening emission rates (see section 2.5). The PB-HAP emitted by facilities in this category include cadmium compounds (97 facilities), chlorinated dibenzodioxins and furans (analyzed as 2,3,7,8- tetrachlorodibenzo-p-dioxin TEQ) (78 facilities ), polychlorinated biphenyls (58 facilities), mercury compounds (106 facilities), and polyc yclic organic matter (POM) (analyzed as benzo(a)pyrene toxicity equivale nce or (TEQ))(113 facilities).

Following the above approach, 1 facility emit ted cadmium compounds above the Tier I screening level, 42 facilities emitting chlorina ted dibenzodioxins and furans as 2,3,7,8- tetrachlorodibenzo-p-dioxin TEQ above the Tier I screening level, 14 facilities emit mercury compounds above the Tier I scr eening level, and 57 facilities emit POM as benzo(a)pyrene TEQ above the screening level. Polychlorinated biphenyl is a PB-HAP but does not currentlyDraft Risk Assessment for the Petroleum Refining Source Sector – FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 43

have a multipathway screening value and so was not evaluated for potential non-inhalation risks. As shown in Table 3.1-1, this HAP is not emitted in appreciable quantities. Due to the theoretical construct of the screening model, th ese factors are not directly translatable into estimates of risk or hazard quotients for these facilities. Table 3.2-3 summarizes the results of this screening (Tier I) comparison.

For the PB-HAP that did not screen out duri ng our initial “Tier I” multipathway screening analysis, we refined our analysis somewhat by including some additional site-specific information to develop an “intermediate” or Tier II screen. (See Appendix 4 of this document for more information about the Tier II screen.) The additional site-specific information included the land use around the facilities, the location of fishable lakes, and local wind direction and speed. The result of this analysis was the development of site-specific emission screening thresholds for cadmium, chlorinated dibenzodioxins and furans, POM and mercury. Based on this Tier II screening analysis, as presented in Table 3.2-3, 1 facility’s emissions remained above the screening threshold for cadmium with the highest at about 2 times the threshold. Emissions from 23 facilities re mained above the thre shold for chlorinated dibenzodioxins and furans with the highest at about 40 times the threshold. No facility’s emissions remained above the threshold for mercury. For POM, 44 facility’s emissions exceeded the site-specific screening threshold for POM with the highest by a factor of about 30. A Tier II exceedance cannot be equated with a risk value or a hazard quotient or index. Rather it represents a high-end estimate of what the risk or hazard may be. For example, an exceedance of 2 for a non carcinogen can be interpreted to mean that we have high confidence that the hazard would be lower than 2. Similarly, an exceedance of 30 for a carcinogen means that we have high confidence that the risk is lower than 30-in-1-million. Our confidence comes from the conservative, or health-protective, assumptions that are in the Tier II screen: we choose inputs from the upper end of the range of possible values for the influential parameters used in the Tier II screen; and we assume that the exposed individual exhibits ingestion behavior that would lead to a high total exposure.

Table 3.2-3. Summary of Multipathway Screening Results for Petroleum Refining Source Sector

Tier I Tier II

PBHAP Num Facilities Emitting

This
PBHAP (142 in

source category) Max Emissions of this PBHAP

(TPY) Max Emissions

of this PBHAP, Div by the Screening

Level Num Facilities Exceeding

the Screening Level of

this
PBHAP Max Emissions

of this PBHAP, Div by the Screening

Level Num Facilities Exceeding

the Screening Level of

this
PBHAP Cadmium Compounds 97 6.05E-02 5 1 2 1

Chlorinated Dibenzodioxins and Furans as 2,3,7,8- Tetrachlorodibenzo-p-dioxin 78 2.81E-09 100 42 40 23

Divalent Mercury 106 1.73E-03 5 14 1 0 Polycyclic Organic Matter as Benzo(a)Pyrene 113 2.58E-03 400 57 30 44Draft Risk Assessment for the Petroleum Refining Source Sector – FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 44

In evaluating the potential for multipathway effects from emissions of lead, modeled maximum annual lead concentrations were compared to the NAAQS for lead (0.15 μg/m 3 ). Lead emissions were reported from 103 facilities. Results of this analysis estimate that with the exception of one facility, the NAAQS for lead would not be exceeded by any facility. At the one facility, the lead NAAQS is exceeded 1.5 tim es at an on-site location. Off-site at this facility, the concentrations are below the NAAQS.

To gain a better understanding of the uncertainty associated with the multipathway Tier I and 2 screening analysis, a more refined multipathway case study using the TRIM.Fate model was conducted for a single petroleum refinery. The site, Marathon Ashland Petroleum facility (NEI6087) near Garyville in St. John the baptist Parish, Louisiana, was among those that exceeded the Tier I screen for any PB-HAP and it was among the highest of the refineries that exceeded a Tier II threshold for any PB-HAP. It also was selected based on the feasibility, with respect to the modeling framework, of obtaining model parameters for the region surrounding the refinery. The expo sure scenarios assessed for the selected facility are not necessarily representative of the highest pote ntial exposures for human receptors from the petroleum refineries sector, but the results for exposures (and the risks calculated for those exposures) are anticipated to be among the highest that might be encountered for this source category. The approach, data, assumptions, and results of the site-specific assessment are presented in Appendix 10 ( Technical Support Document: Human Health Multipathway Residual Risk Assessment for the Petroleum Refineries Sector – A site Specific Analysis ) of this document.

As in the screening assessments, hypothetical farmer and angler scenarios were included in the site-specific assessment, as well as children in different age ranges and adults. It is important to note that even during the refined multipathway analysis that no data exists to verify existence of either the farmer or angler scenario outlined below. These scenarios are included as part of the exercise of evaluating the uncertainty associated with the multipathway screens. The farmer scenario involves an indi vidual living for a 70-year lifetime on a farm homestead in the vicinity of the source and consuming produce grown on, and meat and animal products raised on, the farm. The individual also incidentally ingests surface soil at the location of the farm homestead. The angler scenario involves an individual who regularly consumes fish caught in freshwater lakes in the vicinity of the source of interest over the course of a 70-year lifetime. In addition, exposure estimates and risks for infants consuming contaminated breast milk were evaluated in the case of dioxins, with the assumption that the nursing mother was exposed to chemicals via one of the two basic scenarios described above.

Variations of these two scenarios were evaluated using different assumptions regarding food source (i.e., location of the farm homestead or the water body from which fish are obtained), the age of the individual exposed (for non-cancer hazards), the assumed ingestion rate of each food type, and other factors. In particular, a range of fish ingestion rates was evaluated to determine the possible health risks associated with that pathway.

Cancer risk estimates, as presented in table 3.2-4, from exposure to PAHs include an incremental lifetime cancer risk of 2 in 1 million, with farm exposure at 90 th percentileDraft Risk Assessment for the Petroleum Refining Source Sector – FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 45

ingestion rates driving the risk. Cancer results from exposure to dioxins also include an incremental lifetime cancer risk of 2 in 1 milli on, with fish exposure by the adult female angler at 99 th percentile ingestion rates driving the risk. Noncancer HQs, also presented in Table 3.2-4, did not exceed 1 for any scenario for exposure to mercury or cadmium. For methyl mercury, the highest HQ was 0.04 with the Child Age 1-2 group driving the risk, while the highest noncancer HQ for cadmium was 0.02, also with the Child Age 1-2 group driving the risk. As explained in Appendix 4, the scenarios included in the screening and refined assessments include children at different age ranges, as well as adults, and the Child Age 1-2 group has the highest risks for the noncancer endpoints [ 40 ].

Table 3.2-4. Summary of Refined Multipathway Results for Marathon Ashland Petroleum facility (NEI6087) St. John the Baptist Parish, Louisiana

PB

‐ HAP Group Ratio of Emissions to the

Emission Screening

Threshold Site-specific Hazard Quotient or Risk

Tier I Tier II Via Fish Only, Lac des

Allemands Via Farm Only,
Farm_W_S Combined Fish + Farm

Mercury (non

‐ cancer) 2 0.3 0.04 0.0001 0.04

Cadmium (non

‐ cancer) 0.3 0.05 0.02 0.0007 0.02

PAHs (cancer) 100 20 0.4 2 2

Dioxins (cancer) 40 5 1 0.5 2

Total Incremental Lifetime

Cancer Risk (PAH+Dioxin) 200 30 1 2 4

The site-specific analysis for this facility showed that the potential risk was lower than the Tier II screen for each pollutant over-predicted the potential risk when compared to the refined analysis results. In the site-specific analysis, for mercury emissions, the potential HQ was about 7 times lower than the exceedance of the Tier II screening threshold. For cadmium emissions, the site-specific results (HQ) are about 3 times lower than the exceedance of the Tier II screening threshold. For PAH emissions, the site-specific analysis had a cancer risk 14 times the exceedance of the Tier II screening threshold. Finally, for the site-specific analysis, the cancer risk from emissions of dioxins is 3 times lower than the exceedance of the Tier II screening threshold. Formatted TableDraft Risk Assessment for the Petroleum Refining Source Sector – FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 46

While this more refined assessment was only perfor med on a single facility, the results of this analysis lead us to the conclusion that if refined analyses were performed for other sites, the risk estimates would consistently be lower than those estimated by the Tier1 and 2 analyses. As shown in Table 3.2.-5, the overall trend when the analyses rely less on defaults and more on site-specific data is an increase in the emissions threshold. For example, between the Tier I and Tier II screens, the screening threshold for mercury emissions increased by 6 times. For this source category, the threshold for mercury increased by 45 times between the Tier I screen and the site-specific assessment.

Table 3.2 – 5. Summary of Trends in Threshold Exceedances for the Multipathway Screening and Site-Specific Analyses

PB-HAP Group Increase in Emissions Threshold (or Increase in Ration of Emissions to Screening Threshold)

Tier I to Tier II Tier II to SiteSpecific Tier I to SiteSpecific

Mercury 6 7 45

Cadmium 6 3 20

PAHs 4 14 58

Dioxins 8 3 23

Total Cancer 5 9 45

MACT-level Environmenta l Assessment Results

We conducted a screening-level evaluation of the potential adverse environmental risks associated with emissions of the following environmental HAP from the petroleum refineries source category: lead compounds, mercury compounds, cadmium compounds, POM, chlorinated dibenzodioxins and furans, hydrogen chloride, and hydrogen fluoride. The results of the environmental screening analysis are summarized in Table 3.2-6.

In the Tier I screening analysis for PB-HAP (other than lead, which was evaluated differently as noted in section III.A.5 of this preamble) the individual modeled Tier I concentrations for one facility in the source category exceeded soil ecological benchmarks for mercuric chloride. In addition, Tier I modeled concentrations for four facility exceeded sediment and soil ecological benchmarks for PAH. Therefore, we c onducted a Tier II assessment. In the Tier II screening analysis for PB-HAP none of the indi vidual modeled concentra tions for any facility in the source category exceeded any of the eco logical benchmarks (either the LOAEL or NOAEL). For lead, we did not estimate any exceedances of the secondary lead NAAQS.

For hydrogen fluoride and hydrogen chloride, the average modeled concentration around each facility (i.e., the average concentration of all o ff-site data points in the modeling domain) didDraft Risk Assessment for the Petroleum Refining Source Sector – FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 47

not exceed the ecological benchmarks. In addition, each individual modeled concentration of hydrogen chloride and hydrogen fluoride (i.e., each off-site data point in the modeling domain) was below the ecological be nchmarks for all facilities.

Table 3.2-6. Summary of Environmental Risk Screen Results for the Petroleum Refineries Source Category

Environmental
HAP Number of Facilities In Category Exceeding Percent of Modeled Area in Category Exceeding 2

Tier I Screen Tier II Screen 1 NOAEL LOAEL NOAEL LOAEL NOAEL LOAEL

PBHAP Pb None None None None 0% 0% HgCl 2 NA 1 None None 0% 0% Cd None None None None 0% 0%
PAH 4 None None None 0% 0%
Dioxin None None None None 0% 0%
Acid
Gases HF 3 NA None - - NA 0%
HCL 4 NA None - - NA 0%

NA – Not Applicable. 1- Tier II screen is performed for PB-HAP when there are exceedances of the Tier I screen. The acid gas screen is a one tier screen. 2 - A value of 0% indicates that none of the modeled data points exceeded the benchmark. For PB-HAP the percent area is based on the Tier II results, if a Tier II analysis is performed. Otherwise, the percent area is based on the Tier I results. 3 – For HF, we evaluated two benchmarks, one from Canada and the other from the State of Washington. Although, they are both consider ed to be LOELs - the level between a NOAEL and a LOAEL, we have listed the results under the LOAEL column. 4 – For HCL, we evaluated one benchmark at the LOAEL level.

Facility-wide Inhalation Assessment Results

The facility-wide chronic MIR and TOSHI, avai lable in Appendix 5, are based on emissions from all sources at the identified facilities. Th e results of the facility-wide assessment for cancer risks are summarized in Table 3.2-7. The results indicate that 115 facilities with petroleum refining processes (covered by MACT 1 or MACT 2) have a facility-wide cancer MIR greater than or equal to 1 in a million. The maximum facility-wide cancer MIR is 70 in a million, at one facility. The 70 in a million is mainly driven by naphthalene, chromium (VI) compounds, and arsenic compounds from storage tanks, fugitive dust/blasting/construction, and coking unit vents & coke handling processes. The maximum facility-wide TOSHI for the source category is estimated to be 4, at one fac ility, mainly driven by emissions of chlorine from catalytic reforming unit vents and cooling towers.Draft Risk Assessment for the Petroleum Refining Source Sector – FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 48

Table 3.2-7 Source Category Contribution to Facility-Wide Chronic Cancer Risks (Actual Emissions)

Petroleum Refining Source Sector Number of Facilities Binned by Facility-Wide MIR (in 1 million)
Source Sector MIR Contribution to Facility-Wide MIR <1 1 ≤ MIR<10 10 ≤ MIR<100 > 100 Total

> 90% 14 37 38 0 89
50-90% 9 20 12 0 41
10-50% 3 4 3 0 10
< 10% 1 0 1 0 2
Total 27 61 54 0 142



### 3.3 Post-control risk characterization As shown in Table 3.2-1 above, the baseline cancer risks (as shown in the Incidence Driver rows) are primarily driven by coking unit vents and coke handling and process equipment leaks. Given this, using the same risk as sessment methods described above, we estimated what the risks would be if all of the petroleum refinery facilities adopted control measures to limit emissions from coking units, and storage and loading processes in one control scenario. This scenario is described in the memorandum entitled “Post Controlled Modeling Results”, available in the docket. These controls would reduce the cancer risk to the individual most exposed from 60

- in -1 million to 50

- in -1 million based on actual emissions at the facility where storage tank emissions were driving the risk

. However, the post-control MIR remains unchanged for the refinery source category, at 60-in 1-million, because the several facilities combine for an MIR of 60-in-1 million at a census block location. The post-control cancer risk is primarily driven by naphthalene, dibenzo[a,h]anthracene, and benzo[a]pyrene from process equipment leaks. The number of people estimated to have a cancer risk greater than or equal to 10 in a million would be reduced from 100,000 people to 80,000 people. And, the number of people estimated to have a cancer risk greater than or equal to 1 in a million would be reduced from 5,000,000 people to 4,000,000 people. There is a similar percentage reduction in incidence (18 percent) ; however this is not readily apparent with the presentation of the results to one significant figure (0.3 both in the baseline and post-control scenarios).

A closer look at the controls driving the exposed population reductions shows that approximately 7,300 people will have risks less than 10 in a million and 985,000 people will fall below 1 in a million as a result of pro posed delayed coker units (DCU) controls. The number of people with a cancer risk greater th an 10-in-1 million from storage tank emissions will be reduced from 3,000 to 60 and the number of people with a cancer risk greater than 1- in-1 million from storage tank emissions will fall from 140,000 to 72,000 as a result of proposed storage tank controls. Similarly, a clos er look at the controls driving the incidence reductions shows that approximately 15 percent (0.05 incidence) of the incidence reduction is attributable to proposed delayed coker units (DCU) controls and about 3 percent (0.01 incidence) from proposed storage vessel controls. Deleted: equipment leaks,

Deleted: includes an 86 percent reduction in HAP emissions from coking units, a 4 percent reduction in HAP emissions from equipment leaks, and a 51 percent reduction in HAP emissions from storage and loading processes. (The scenario

Deleted: more detail in

Deleted: emissions memo

Deleted: [5]) Based on this control scenario, we estimated (based on use of the same modeling assumptions and methodologies used for the baseline risk assessment) that the maximum individual lifetime

Deleted: for one facility would be reduced

Deleted:

Deleted: a

Deleted:

Deleted: a

Deleted:Draft Risk Assessment for the Petroleum Refining Source Sector – FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 49

The results of the post-control assessment also indicate that, based on actual emissions, there would be no reduction in chronic noncancer inhalation values because we did not identify any cost effective control technologies that would further reduce these risks. There is a slight reduction in acute noncancer inhalation values, with the maximum off-site HQ reduced from 5 (actual emissions) to 3 (post-control emissions) , based on the REL for nickel compounds.

Considering post-control emissions of multipathw ay HAP, there are expected to be minimal reductions in PB-HAP with the control scenario (see Appendix 5 - Table 4b).



### 4 General discussion of uncertai nties and how they have been addressed

4.1 Exposure modeling uncertainties Although every effort has been made to identify all the relevant facilities and emission points, as well as to develop accurate estimates of the annual emission rates for all relevant HAP, the uncertainties in our emission inventory likely dominate the uncertainties in the exposure analysis. The chronic ambient modeling uncertainties are considered relatively small in comparison, since we are using EPA’s refined local dispersion model with site-specific parameters and reasonably representative meteorology. If anything, the population exposure estimates are biased high by not accounting for short- or long-term population mobility, and by neglecting processes like deposition, plum e depletion, and atmospheric degradation. Additionally, estimates of the MIR contain uncertainty, because they are derived at census block centroid locations rather than actual resi dences. This uncertainty is known to create potential underestimates and overestimates of th e actual MIR values for individual facilities, but, overall, it is not thought to have a significant impact on the estimated MIR for a source category. Finally, we did not factor in the possibility of a source closure occurring during the 70-year chronic exposure period, leading to a potential upward bias in both the MIR and population risk estimates; nor did we factor in the possibility of population growth during the 70-year chronic exposure period, leading to a potential downward bias in both the MIR and population risk estimates.

A sensitivity analysis performed for the 1999 NATA found that th e selection of the meteorology dataset location could result in a range of chronic ambient concentrations which varied from as much as 17 percent below the predicted value to as much as 84 percent higher than the predicted value. This variability tran slates directly to the predicted exposures and risks in our assessment, indicating that the actual risks could vary from 17 percent lower to 84 percent higher than the predicted values.

We have purposely biased the acute screening results high, considering that they depend upon the joint occurrence of independent factors, such as hourly emissions rates, meteorology and human activity patterns. Furthermore, in cases where multiple acute threshold values are considered scientifically acceptable we have chosen the most conservative of these threshold values, erring on the side of overestimating potenti al health risks from acute exposures. In theDraft Risk Assessment for the Petroleum Refining Source Sector – FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 50

cases where these results indicated the potential for exceeding short-term health thresholds, we refined our assessment by developing a better understanding of the geography of the facility relative to potential exposure locations.



### 4.2 Uncertainties in the dose-response relationships In the sections that follow, separate discussi ons are provided on uncertainty associated with cancer potency factors and for noncancer reference values. Cancer potency values are derived for chronic (lifetime) exposures. Noncancer reference values are generally derived forchronic exposures (up to a lifetime), but may also be derived for acute (<24 hours), shortterm (>24 hours up to 30 days), and subchronic (>30 days up to 10 percent of lifetime) exposure durations, all of which are derived based on an assumption of continuous exposure throughout the duration specified. For the purposes of assessing all potential health risks associated with the emissions included in an assessment, we rely on both chronic (cancer and noncancer) and acute (noncancer) benchmarks, which are described in more detail below.

Although every effort is made to identify peer-reviewed dose-response values for all HAP emitted by the source category included in an assessment, some HAP have no peer-reviewed cancer potency values or reference values for chronic non-cancer or acute effects (inhalation or ingestion). Since exposure s to these pollutants cannot be included in a quantitative risk estimate, an understatement of risk for these pollutants at environmental exposure levels is possible.

Additionally, chronic dose-response values for certain compounds included in the assessment may be under EPA IRIS review and revised assessments may determine that these pollutants are more or less potent than cu rrently thought. We will re-evaluat e risks if, as a result of these reviews, a dose-response metric changes enough to indicate that the risk assessment may significantly mischaracterize human health risk

Cancer assessment

The discussion of dose-response uncertainties in the estimation of cancer risk below focuses on the uncertainties associated with the specific approach currently used by the EPA to develop cancer potency factors. In general, these same uncertainties attend the development of cancer potency factors by CalEPA, the s ource of peer-reviewed cancer potency factors used where EPA-developed values are not yet avai lable. To place this discussion in context, we provide a quote from the EPA’s Guidelines for Carcinogen Risk Assessment [ 41 ] (herein referred to as Cancer Guidelines ). “The primary goal of EPA actions is protection of human health; accordingly, as an Agency policy, risk assessment procedures, including default options that are used in the absence of scien tific data to the contrary, should be health protective.” The approach adopted in this document is consistent with this approach as described in the Cancer Guidelines

.

For cancer endpoints EPA usually derives an oral slope factor for ingestion and a unit risk value for inhalation exposures. These values allow estimation of a lifetime probability of developing cancer given long-term exposures to the pollutant. Depending on the pollutant being evaluated, EPA relies on both animal bioassay and epidemiological studies toDraft Risk Assessment for the Petroleum Refining Source Sector – FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 51

characterize cancer risk. As a science policy approach, consistent with the Cancer Guidelines , EPA uses animal cancer bioassays as indicators of potential human health risk when other human cancer risk data are unavailable.

Extrapolation of study data to estimate pote ntial risks to human populations is based upon EPA’s assessment of the scien tific database for a pollutant using EPA’s guidance documents and other peer-reviewed methodologies. The EPA Cancer Guidelines describes the Agency’s recommendations for methodologies for cancer risk assessment. EPA believes that cancer risk estimates developed following the procedures described in the Cancer Guidelines and outlined below generally provide an upper bound estimate of risk. That is, EPA’s upper bound estimates represent a “plausible upper limit to the true value of a quantity” (although this is usually not a true statistical confidence limit). 24 In some circumstances, the true risk could be as low as zero; however, in other ci rcumstances the risk could also be greater. 25 When developing an upper bound estimate of risk and to provide risk values that do not underestimate risk, EPA generally relies on conservative default approaches. 26 EPA also uses the upper bound (rather than lower bound or central) estimates in its assessments, although it is noted that this a pproach can have limitations for so me uses (e.g. priority setting, expected benefits analysis).

Such health risk assessments have associated uncertainties, some which may be considered quantitatively, and others which generally are expressed qualitatively. Uncertainties may vary substantially among cancer risk assessments associ ated with exposures to different pollutants, since the assessments employ different database s with different strengths and limitations and the procedures employed may differ in how well they represent actual biological processes for the assessed substance. EPA’s Risk Characterization Handbook also recommends that risk characterizations present estimates demonstrati ng the impact on the assessment of alternative choices, data, models and assumptions [ 42 ]. Some of the major sources of uncertainty and variability in deriving cancer risk values are described more fully below.

(1) The qualitative similarities or differe nces between tumor responses observed in experimental animal bioassays and those which would occur in humans are a source of uncertainty in cancer risk assessment. In general, EPA does not assume that tumor sites observed in an experimental animal bioassay ar e necessarily predictive of the sites at which

24 IRIS glossary (www.epa.gov /NCEA/iris/help_gloss.htm).

25 The exception to this is the URE fo r benzene, which is considered to cover a range of values, each end of which is considered to be equally plausible, and which is based on maximum likelihood estimates.

26 According to the NRC report Science and Judgment in Risk Assessment (NRC, 1994) “[Default] options are generic approaches, based on general scientific knowledg e and policy judgment, that are applied to various elements of the risk-assessment process when the correct scientific model is unknown or uncertain.” The 1983 NRC report Risk Assessment in the Federal Government: Managing the Process defined default option as “the option chosen on the basis of risk assessment policy that appears to be the best choice in the absence of data to the contrary” (NRC, 1983a, p. 63). Therefore, default options are not rules that bind the agency; rather, the agency may depart from them in ev aluating the risks posed by a specific substance when it believes this to be appropriate. In keeping with EPA’s goal of protecting public health and the environment, default assumptions are used to ensure that risk to chemicals is not underestimated (although defaults are not intended to overtly overestimate risk). See EPA 2004 An Examination of EPA Risk Assessment Principles and Practices, EPA/100/B-04/001 available at: http: //www.epa.gov/osa/pdfs/ratf-final.pdf.Draft Risk Assessment for the Petroleum Refining Source Sector – FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 52

tumors would occur in humans. 27 However, unless scientific support is available to show otherwise, EPA assumes that tumors in animals are relevant in humans, regardless of target organ concordance. For a specific pollutant, qualitative differences in species responses can lead to either under-estimation or over-estimation of human cancer risks.

(2) Uncertainties regarding the most appropriate dose metric for an assessment can also lead to differences in risk predictions. For example, the measure of dose is commonly expressed in units of mg/kg/d ingested or the inhaled concentration of the pollutant. However, data may support development of a pharmacokinetic model for the absorption, distribution, metabolism and excretion of an agent, which may result in improved dose metrics (e.g., average blood concentration of the pollutant or the quantity of agent metabolized in th e body). Quantitative uncertainties result when the appropriate choice of a dose metric is uncertain or when dose metric estimates are themselves uncertain (e.g., as can occur when alternative pharmacokinetic models are available for a compound). Uncertainty in dose estimates may lead to either over or underestimation of risk.

(3) For the quantitative extrapolation of cancer risk estimates from experimental animals to humans, EPA uses scaling methodologies (rela ting expected response to differences in physical size of the species), which introduce another source of uncertainty. These methodologies are based on both biological data on differences in rates of process according to species size and empirical comparisons of toxicity between experimental animals and humans. For a particular pollutant, the quantitative difference in cancer potency between experimental animals and humans may be either greater than or less than that estimated by baseline scientific scaling predictions due to un certainties associated with limitations in the test data and the correctness of scaled estimates.

(4) EPA cancer risk estimates, whether based on epidemiological or experimental animal data, are generally developed using a benchmark dose (BMD) analysis to estimate a dose at which there is a specified excess risk of cancer, which is used as the point of departure (or POD) for the remainder of the calculation. Statistical uncertainty in developing a POD using a benchmark dose (BMD) approach is generally addressed though use of the 95 percent lower confidence limit on the dose at which the specified excess risk occurs (the BMDL), decreasing the likelihood of understating risk. EPA has generally utilized the multistage model for estimation of the BMDL using cancer bioassay data (see further discussion below).

(5) Extrapolation from high to low doses is an important, and potentially large, source of uncertainty in cancer risk assessment. EPA uses different approaches to low dose risk assessment (i.e., developing estimates of risk for exposures to environmental doses of an agent from observations in experimental or ep idemiological studies at higher dose) depending on the available data and understanding of a pollutant’s mode of action (i.e., the manner in which a pollutant causes cancer). EPA’s Cancer Guidelines express a preference for the use of reliable, compound-specific, biologically-based risk models when feasible; however, such

27 Per the EPA Cancer Guidelines: “The default option is that positive effects in animal cancer studies indicate that the agent under study can have carcinogenic potential in humans.” and “Target organ concordance is not a prerequisite for evaluating the implications of animal study results for humans.”Draft Risk Assessment for the Petroleum Refining Source Sector – FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 53

models are rarely available. The mode of action for a pollutant (i.e., the manner in which a pollutant causes cancer) is a key consideration in determining how risks should be estimated for low-dose exposure. A reference value is calculated when the available mode of action data show the response to be nonlinear (e.g., as in a threshold response). A linear low-dose (straight line from POD) approach is used when available mode of action data support a linear (e.g., nonthreshold response) or as the most common default approach when a compound’s mode of action is unknown. Linear extrapolatio n can be supported by both pollutant-specific data and broader scientific considerations. For example, EPA’s Cancer Guidelines generally consider a linear dose-response to be appropriate for pollutants that interact with DNA and induce mutations. Pollutants whose effects are ad ditive to background biological processes in cancer development can also be predicted to have low-dose linear responses, although the slope of this relationship may not be the same as the slope estimated by the straight line approach.

EPA most frequently utilizes a linear low-dose extrapolation approach as a baseline sciencepolicy choice (a “default”) when available data do not allow a compound-specific determination. This approach is designed to no t underestimate risk in the face of uncertainty and variability. EPA believes that linear dos e-response models, when appropriately applied as part of EPA’s cancer risk assessment process, provide an upper bound estimate of risk and generally provide a health protective approach. Note that another source of uncertainty is the characterization of low-dose nonlinear, non-thre shold relationships. The National Academy of Sciences has encouraged the exploration of sigmoidal type functions (e.g., log-probit models) in representing dose response relationships due to the variability in response within human populations. Another National Research Council (NRC) report [ 43 ] suggests that models based on distributions of individual thresh olds are likely to lead to sigmoidal-shaped dose-response functions for a po pulation. This report notes sources of variability in the human population: “One might expect these individual tolerances to vary extensively in humans depending on genetics, coincident e xposures, nutritional status, and various other susceptibility factors...” Thus, if a distribution of thresholds approach is considered for a carcinogen risk assessment, ap plication would depend on abilit y of modeling to reflect the degree of variability in response in human populations (as opposed to responses in bioassays with genetically more uniform rodents). Note also that low dose linearity in risk can arise for reasons separate from population variability: due to the nature of a mode of action and additivity of a chemical’s effect on top of background chemical exposures and biological

processes.

As noted above, EPA’s current approach to cancer risk assessment typically utilizes a straight line approach from the BMDL. This is equivalent to using an upper confidence limit on the slope of the straight line extrapolation. The impact of the choice of the BMDL on bottom line risk estimates can be quantified by comparing risk estimates using the BMDL value to central estimate BMD values, although these differences are generally not a large contributor to uncertainty in risk assessment (Subramaniam et. al., 2006) [ 44 ]. It is important to note that earlier EPA assessments, including the majority of those for which risk values exist today, were generally developed using the multistage model to extrapolate down to environmental dose levels and did not involve the use of a POD. Subramaniam et. al. (2006) also provide comparisons indicating that slopes based on st raight line extrapolation from a POD do notDraft Risk Assessment for the Petroleum Refining Source Sector – FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 54

show large differences from those based on the upper confidence limit of the multistage model.

(6) Cancer risk estimates do not generally make specific adjustments to reflect the variability in response within the human population — resulting in another source of uncertainty in assessments. In the diverse human population, some individuals are likely to be more sensitive to the action of a carcinogen than th e typical individual, although compound-specific data to evaluate this variability are generally not available. There may also be important life stage differences in the quantitative potency of carcinogens and, with the exception of the recommendations in EPA’s Supplemental Cancer Guidance for carcinogens with a mutagenic mode of action, risk assessments do not generally quantitatively address life stage differences. However, one approach used commonly in EPA assessments that may help address variability in response is to extrapolate human response fr om results observed in the most sensitive species and sex tested, resulting typically in the highest URE which can be supported by reliable data, thus supporting estimates that are designed not to underestimate risk in the face of uncertainty and variability.

Chronic noncancer assessment

Chronic noncancer reference values represent chronic exposure levels that are intended to be health-protective. That is, EPA and other organizations which develop noncancer reference values (e.g., the Agency for Toxic Substances and Disease Registry – ATSDR) utilize an approach that is intended not to underestimate risk in the face of uncertainty and variability. When there are gaps in the available information, uncertainty factors (UFs) are applied to derive reference values that are intended to be protective against appreciable risk of deleterious effects. Uncertainty factors are commonly default values 28 e.g., factors of 10 or 3, used in the absence of compound-specific data; where data are available, uncertainty factors may also be developed using compound-specific information. When data are limited, more assumptions are needed and more default factors are used. Thus there may be a greater tendency to overestimate risk—in the sense that further study might support development of reference values that are higher (i.e., less potent) because fewer default assumptions are needed. However, for some pollutants it is possible that risks may be underestimated.

28 According to the NRC report Science and Judgment in Risk Assessment (NRC, 1994) “[Default] options are generic approaches, based on general scientific knowledg e and policy judgment, that are applied to various elements of the risk-assessment process when the correct scientific model is unknown or uncertain.” The 1983 NRC report Risk Assessment in the Federal Go vernment: Managing the Process defined default option as “the option chosen on the basis of risk assessment policy that appears to be the best choice in the absence of data to the contrary” (NRC, 1983a, p. 63). Therefore, default options are not rules that bind the agency; rather, the agency may depart from them in eval uating the risks posed by a specific substance when it believes this to be appropriate. In keeping with EPA’s goal of protecting public health and the environment, default assumptions are used to ensure that risk to chemicals is not underestimated (although defaults are not intended to overtly overestimate risk). See EPA 2004 An examination of EPA Risk Asse ssment Principles and Practices, EPA/100/B-04/001 available at: http://www.epa.gov/osa/pdfs/ratf-final.pdf.Draft Risk Assessment for the Petroleum Refining Source Sector – FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 55

For non-cancer endpoints related to chronic exposures, EPA derives a Reference Dose (RfD) for exposures via ingestion, and a Reference Concentration (RfC) for inhalation exposures. These values provide an estimate (with uncertainty spanning perhaps an order of magnitude) of daily oral exposure (RfD) or of a continuous inhalation exposure (RfC) to the human population (including sensitive subgroups) that is li kely to be without an appreciable risk of deleterious effects during a lifetime. 29 To derive values that are intended to be “without appreciable risk,” EPA’s methodology relies upon an uncertainty factor (UF) approach [ 45 ], [ 46 ] which includes consideration of both uncertainty and variability.

EPA begins by evaluating all of the availabl e peer-reviewed literatu re to determine noncancer endpoints of concern, evaluating the qual ity, strengths and limitations of the available studies. EPA typically chooses the relevant endp oint that occurs at the lowest dose, often using statistical modeling of the available data , and then determines the appropriate point of departure (POD) for derivation of the reference value. A POD is determined by (in order of preference): (1) a statistical estimation using th e benchmark dose (BMD) approach; (2) use of the dose or concentration at which the toxi c response was not significantly elevated (no observed adverse effect level— NOAEL); or (3) use of the lowest observed adverse effect level (LOAEL).

A series of downward adjustments using default UFs is then applied to the POD to estimate the reference value [ 47 ]. While collectively termed “UFs”, these factors account for a number of different quantitative considerations when u tilizing observed animal (usually rodent) or human toxicity data in a risk assessment. The UFs are intended to account for: (1) variation in susceptibility among the members of the human population (i.e., inter-individual variability); (2) uncertainty in extrapolating from experimental animal data to humans (i.e., interspecies differences); (3) uncertainty in extrapolating from data obtained in a study with less-than-lifetime exposure (i.e., extrapol ating from subchronic to chronic exposure); (4) uncertainty in extrapolating from a LOAEL in the absence of a NOAEL; and (5) uncertainty when the database is incomplete or there are problems with applicability of available studies. When scientifically sound, peer-reviewed assessment-specific data are not available, default adjustment values are selected for the individual UFs. For each type of uncertainty (when relevant to the assessment), EPA typically applies an UF value of 10 or 3 with the cumulative UF value leading to a down ward adjustment of 10-3000 fold from the selected POD. An UF of 3 is used when the data do not support the use of a 10-fold factor. If an extrapolation step or adjustment is not relevant to an assessment (e.g., if applying human toxicity data and an interspecies extrapolation is not required) the associated UF is not used. The major adjustment steps are described more fully below.

1) Heterogeneity among humans is a key source of variability as well as uncertainty. Uncertainty related to human variation is considered in extrapolating doses from a subset or smaller-sized population, often of one sex or of a narrow range of life stages (typical of occupational epidemiologic studies), to a larger, more diverse population. In the absence of pollutant-specific data on human variation, a 10 -fold UF is used to account for uncertainty associated with human variation. Human variation may be larger or smaller; however, data to

29 See IRIS glossaryDraft Risk Assessment for the Petroleum Refining Source Sector – FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 56

examine the potential magnitude of human variability are often unavailable. In some situations, a smaller UF of 3 may be applied to reflect a known lack of significant variability among humans.

2) Extrapolation from results of studies in experimental animals to humans is a necessary step for the majority of chemical risk assessments. When interpreting animal data, the concentration at the POD (e.g. NOAEL, BMDL ) in an animal model (e.g. rodents) is extrapolated to estimate the human response. While there is long-standing scientific support for the use of animal studies as indicators of potential toxicity to humans, there are uncertainties in such extrapolations. In the absence of data to the contrary, the typical approach is to use the most relevant endpoi nt from the most sensitive species and the most sensitive sex in assessing risks to the average human. Typically, compound specific data to evaluate relative sensitivity in humans versus rodents are lacking, thus leading to uncertainty in this extrapolation. Size-re lated differences (allometric relationships) indicate that typically humans are more sensitive than rodents when compared on a mg/kg/day basis. The default choice of 10 for the interspecies UF is cons istent with these differences. For a specific chemical, differences in species responses may be greater or less than this value.

Pharmacokinetic models are useful to examine species differences in pharmacokinetic processing and associated uncertainties; howeve r, such dosimetric adjustments are not always possible. Information may not be availabl e to quantitatively assess toxicokinetic or toxicodynamic differences between animals and humans, and in many cases a 10-fold UF (with separate factors of 3 for toxicokinetic and toxicodynamic components) is used to account for expected species differences and associated uncertainty in extrapolating from laboratory animals to humans in the derivation of a reference value. If information on one or the other of these components is available and accounted for in the cross-species extrapolation, a UF of 3 may be used for the remaining component.

3) In the case of reference values for chronic exposures where only data from shorter durations are available (e.g., 90-day subchronic st udies in rodents) or when such data are judged more appropriate for development of an RfC, an additional UF of 3 or 10-fold is typically applied unless the available scientific information supports use of a different value.

4) Toxicity data are typically limited as to the dose or exposure levels that have been tested in individual studies; in an animal stud y, for example, treatment groups may differ in exposure by up to an order of magnitude. The preferred approach to arrive at a POD is to use BMD analysis; however, this approach requires adequate quantitative re sults for a meaningful analysis, which is not always possible. Use of a NOAEL is the next preferred approach after BMD analysis in determining a POD for deriving a health effect reference value. However, many studies lack a dose or exposure level at which an adverse effect is not observed (i.e., a NOAEL is not identified). When using data li mited to a LOAEL, a UF of 10 or 3-fold is often applied.

5) The database UF is intended to account for the potential for deriving an underprotective RfD/RfC due to a data gap preventing complete characterization of theDraft Risk Assessment for the Petroleum Refining Source Sector – FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 57

chemical’s toxicity. In the absence of studies for a known or suspected endpoint of concern, a UF of 10 or 3-fold is typically applied.

There is no RfD or other comparable chronic health benchmark value for lead compounds. Thus, to address multipathway human health and environmental risks associated with emissions of lead from this facility, ambient lead concentrations we re compared to the NAAQS for lead. In developing the NAAQS for lead, EPA considered human health evidence reporting adverse health effects associ ated with lead exposure, as well as an EPA conducted multipathway risk assessment that ap plied models to estimate human exposures to air-related lead and the associated risk (73FR at 66979). EPA also explicitly considered the uncertainties associated with both the huma n health evidence and the exposure and risk analyses when developing the NAAQS for lead. For example, EPA considered uncertainties in the relationship between ambient air lead and blood lead levels (73FR at 66974), as well as uncertainties between blood lead levels and loss of IQ points in children (73FR at 66981). In considering the evidence and risk analyses and their associated uncertainties, the EPA Administrator noted his view that there is no ev idence- or risk-based br ight line that indicates a single appropriate level. Instead, he noted, there is a collection of scientific evidence and judgments and other information, including information about the uncertainties inherent in many relevant factors, which needs to be cons idered together in making this public health policy judgment and in selecting a standard level from a range of reasonable values (73FR at 66998). In so doing, the Administrator decided th at, a level for the primary lead standard of
0.15 μ g/m 3 , in combination with the specified choice of indicator, averaging time, and form, is requisite to protect public health, including the health of sensitive groups, with an adequate margin of safety (73FR at 67006). A thorough discussion of the health evidence, risk and exposure analyses, and their associated uncertainties can be found in EPA’s final rule revising the lead NAAQS (73 FR 66970- 66981, November 12, 2008).

We also note the uncertainties associated w ith the health-based (i.e., primary) NAAQS are likely less than the uncertainties associated with dose-response values developed for many of the other HAP, particularly those HAP for which no human health data exist. In 1988, EPA’s IRIS program reviewed the health effects data regarding lead and its inorganic compounds and determined that it would be inappropriate to develop an RfD for these compounds, saying, “A great deal of information on the health effects of lead has been obtained through decades of medical observation and scientific research. This information has been assessed in the development of air and water quality criteria by the Agency's Office of Health and Environmental Assessment (OHEA) in support of regulatory decision-making by the Office of Air Quality Planning and Standards (OAQPS) and by the Office of Drinking Water (ODW). By comparison to most other environmental toxicants, the degree of uncertainty about the health effects of lead is quite low. It appears that some of these effects, particularly changes in the levels of certain blood enzymes and in aspects of children's neurobehavioral development, may occur at blood lead levels so low as to be essentially without a threshold. The Agency's RfD Work Group discussed inorganic lead (and lead compounds) at two meetings (07/08/1985 and 07/22/1985) and consider ed it inappropriate to develop an RfD for inorganic lead.” EPA’s IRIS assessment for Lead and compounds (inorganic) (CASRN 7439- 92-1), http://www.epa.gov/iris/subst/0277.htm

.Draft Risk Assessment for the Petroleum Refining Source Sector – FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 58

We note further that because of the multi-pathway, multi-me dia impacts of lead, the risk assessment supporting the NAAQS considered dir ect inhalation exposu res and indirect airrelated multi-pathway exposure s from industrial sources like primary and secondary lead smelting operations. It also considered background lead exposures from other sources (like contaminated drinking water and exposure to lead-based paints). In revising the NAAQS for lead, we note that the Administrator placed more weight on the evidence-based framework and less weight on the results from the risk assessment, although he did find the risk estimates to be roughly consistent with and generall y supportive of the evidence-based framework applied in the NAAQS determination (73FR at 67004). Thus, when revising the NAAQS for lead to protect public health with an adequate margin of safety, EPA considered both the evidence-based framework and the risk asse ssment, albeit to different extents.

Acute noncancer assessment

Many of the UFs used to account for variability and uncertainty in the development of acute reference values are quite similar to those de veloped for chronic durations, but more oftenusing individual UF values that may be less than 10. UFs are applied based on chemicalspecific or health effect-specific informati on (e.g., simple irritation effects do not vary appreciably between human individuals, hence a value of 3 is typically used), or based on the purpose for the reference value (see the following paragraph). The UFs applied in acute reference value derivation include: 1) heterogeneity among humans; 2) uncertainty in extrapolating from animals to humans; 3) uncer tainty in LOAEL to NOAEL adjustments; and 4) uncertainty in accounting for an incomplete database on toxic effects of potential concern. Additional adjustments are often applied to account for uncertainty in extrapolation from observations at one exposure duration (e.g., 4 hours) to arrive at a POD for derivation of an acute reference value at another exposure duration (e.g., 1 hour).

Not all acute reference values are developed for the same purpose and care must be taken when interpreting the results of an acute asse ssment of human health effects relative to the reference value or values being exceeded. Where relevant to the estimated exposures, the lack of threshold values at different levels of severity should be factored into the risk characterization as potential uncertainties.Draft Risk Assessment for the Petroleum Refining Source Sector – FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 59



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